From cd2fd930098b3fd1b52e7cf1f18e640f610bd84e Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 11:45:58 -0600 Subject: [PATCH 1/9] build vignette separately --- readme.Rmd => README.Rmd | 0 docs/dev/articles/healthcareai.html | 12 +- docs/dev/index.html | 8 +- .../figure-html/unnamed-chunk-3-1.png | Bin 73364 -> 70036 bytes docs/dev/reference/flash_models.html | 4 +- docs/dev/reference/hcai_impute.html | 13 +- docs/dev/reference/machine_learn.html | 6 +- docs/dev/reference/predict.model_list.html | 2 +- {vignettes => inst/doc}/healthcareai.R | 0 inst/doc/healthcareai.Rmd | 174 ++++++++++++++++++ {vignettes => inst/doc}/healthcareai.html | 12 +- 11 files changed, 207 insertions(+), 24 deletions(-) rename readme.Rmd => README.Rmd (100%) rename {vignettes => inst/doc}/healthcareai.R (100%) create mode 100644 inst/doc/healthcareai.Rmd rename {vignettes => inst/doc}/healthcareai.html (99%) diff --git a/readme.Rmd b/README.Rmd similarity index 100% rename from readme.Rmd rename to README.Rmd diff --git a/docs/dev/articles/healthcareai.html b/docs/dev/articles/healthcareai.html index c9c9e7128..124a82ebc 100644 --- a/docs/dev/articles/healthcareai.html +++ b/docs/dev/articles/healthcareai.html @@ -133,7 +133,7 @@

#> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 11:00:11 +#> Models Trained: 2018-04-02 11:41:37 #> #> Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. #> Best model: Random Forest @@ -146,7 +146,7 @@

Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

predictions <- predict(quick_models)
 predictions
-#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:00:11
+#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:41:37
 #>  Performance in training: ROC = 0.84
 #>  # A tibble: 768 x 14
 #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
@@ -236,7 +236,7 @@ 

#> Performance Metric: PR #> Number of Observations: 692 #> Number of Features: 13 -#> Models Trained: 2018-04-02 11:00:37 +#> Models Trained: 2018-04-02 11:42:03 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -253,7 +253,7 @@

Prediction

predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

predict(models)
-#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:00:34
+#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:42:00
 #>  Performance in training: PR = 0.9
 #>  # A tibble: 692 x 15
 #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
@@ -285,7 +285,7 @@ 

#> Running cross validation for Random Forest #> Running cross validation for k-Nearest Neighbors summary(regression_models) -#> Models trained: 2018-04-02 11:00:50 +#> Models trained: 2018-04-02 11:42:16 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best performance: RMSE = 9.07 @@ -331,7 +331,7 @@

#> Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. #> weight_class: ??? #> Prepping data based on provided recipe -#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:00:50 +#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:42:16 #> Performance in training: RMSE = 9.07 #> # A tibble: 1 x 9 #> predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin diff --git a/docs/dev/index.html b/docs/dev/index.html index 9d777463b..ef3b7e5b0 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -127,15 +127,15 @@

# > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 10:58:57 +# > Models Trained: 2018-04-02 11:40:18 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest -# > ROC = 0.84 +# > ROC = 0.85 # > Optimal hyperparameter values: -# > mtry = 2 +# > mtry = 3 # > splitrule = extratrees -# > min.node.size = 8

+# > min.node.size = 6

Make predictions and examine predictive performance:

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Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -232,7 +232,7 @@

    Examp kernel = "gaussian" ) ) - )
    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 10:59:06 + )
    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 11:40:32 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best algorithm: Random Forest with ROC = 0.84 diff --git a/docs/dev/reference/hcai_impute.html b/docs/dev/reference/hcai_impute.html index 246fdbf6c..08eb9d248 100644 --- a/docs/dev/reference/hcai_impute.html +++ b/docs/dev/reference/hcai_impute.html @@ -179,8 +179,17 @@

    Value

    Examples

    -
    library(recipes) - +
    library(recipes)
    #> Loading required package: dplyr
    #> +#> Attaching package: ‘dplyr’
    #> The following object is masked from ‘package:testthat’: +#> +#> matches
    #> The following objects are masked from ‘package:stats’: +#> +#> filter, lag
    #> The following objects are masked from ‘package:base’: +#> +#> intersect, setdiff, setequal, union
    #> Loading required package: broom
    #> +#> Attaching package: ‘recipes’
    #> The following object is masked from ‘package:stats’: +#> +#> step
    n = 100 set.seed(9) d <- tibble::tibble(patient_id = 1:n, diff --git a/docs/dev/reference/machine_learn.html b/docs/dev/reference/machine_learn.html index 7c63e081d..4211befb1 100644 --- a/docs/dev/reference/machine_learn.html +++ b/docs/dev/reference/machine_learn.html @@ -201,7 +201,7 @@

    Examp # Clean and prep the data, tune algorithms over hyperparameter values to predict diabetes diabetes_models <- machine_learn(training_data, outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make predictions (predicted probability of diabetes) on test data -predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 10:59:13 +predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:40:39 #> Performance in training: ROC = 0.75
    #> # A tibble: 10 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> @@ -222,7 +222,7 @@

    Examp # Predict numeric outcomes simply by specifying the name of the outcome variable age_model <- machine_learn(training_data, outcome = age)

    #> Training new data prep recipe
    #> age looks numeric, so training regression algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # If new data isn't specifed, get predictions on training data. Plot predictions -predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 10:59:16 +predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:40:43 #> Performance in training: RMSE = 9.88
    #> # A tibble: 50 x 17 #> age predicted_age patient_id pregnancies plasma_glucose diastolic_bp #> * <int> <dbl> <int> <int> <int> <dbl> @@ -251,7 +251,7 @@

    Examp #> Performance Metric: ROC #> Number of Observations: 50 #> Number of Features: 13 -#> Models Trained: 2018-04-02 10:59:18 +#> Models Trained: 2018-04-02 11:40:44 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest diff --git a/docs/dev/reference/predict.model_list.html b/docs/dev/reference/predict.model_list.html index d402b4933..6f8e93ea0 100644 --- a/docs/dev/reference/predict.model_list.html +++ b/docs/dev/reference/predict.model_list.html @@ -189,7 +189,7 @@

    Examp models <- machine_learn(pima_diabetes[1:50, ], outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make prediction on the next 20 rows. This uses the best-performing model from # tuning cross validation, and it also prepares the new data in the same way as # the training data was prepared. -predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 10:59:38 +predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:41:05 #> Performance in training: ROC = 0.73
    #> # A tibble: 20 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> diff --git a/vignettes/healthcareai.R b/inst/doc/healthcareai.R similarity index 100% rename from vignettes/healthcareai.R rename to inst/doc/healthcareai.R diff --git a/inst/doc/healthcareai.Rmd b/inst/doc/healthcareai.Rmd new file mode 100644 index 000000000..802c814f4 --- /dev/null +++ b/inst/doc/healthcareai.Rmd @@ -0,0 +1,174 @@ +--- +title: "Getting Started with healthcareai" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Getting Started with healthcareai} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include=FALSE} +set.seed(43170) +knitr::opts_chunk$set(echo = TRUE, results = "hold", collapse = TRUE, + comment = "#> ") +options(tibble.print_min = 5, tibble.print_max = 5) +``` + +First we attach the healthcareai R package to make its functions available. If your package version is less than 2.0, none of the code here will work. You can check the package version with `packageVersion("healthcareai")`, and you can get the latest, cutting-edge development version with `install.packages("remotes"); remotes::install_github("HealthCatalyst/healthcareai-r")`. + +```{r} +library(healthcareai) +``` + +`healthcareai` comes with a built in dataset documenting diabetes among adult Pima females. Once you attach the package, the dataset is available in the variable `pima_diabetes`. Let's take a look at the data with the `str` function. There are 768 records in 10 variables including one identifier column, several nominal variables, and substantial missingness (represented in R by `NA`). + +```{r} +str(pima_diabetes) +``` + +# Easy Machine Learning + +If you don't want to fuss with details any more than necessary, `machine_learn` is the function for you. It makes it as easy as possible to implement machine learning models by putting all the detais in the background so that you don't have to worry about them. Of course it might be wise to worry about them, and we'll get to how to do that further down, but for now, you can automatically take care of problems in the data, do basic feature engineering, and tune multiple machine learning models using cross validation with `machine_learn`. + +`machine_learn` always gets the name of the data frame, then any columns that should not be used by the model (uninformative columns, such as IDs), then the variable to be predicted with `outcome =`. If you want `machine_learn` to run faster, you can have that---at the expense of a bit of predictive power---by setting its `tune` argument to `FALSE`. + +```{r} +quick_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes) +``` + +`machine_learn` has told us that it has created a recipe for data preparation (this allows us to do exactly the same data cleaning and feature engineering when you want predictions on a new dataset), is ignoring `patient_id` when tuning models as we told it to, is training classification algorithms because the outcome variable `diabetes` is categorical, and has executed cross validation for two machine learning models: random forests, and k-nearest neighbors. Let's see what the models look like. + +```{r} +quick_models +``` + +Everything looks as expected, and the best model is is a random forest that achives performance of AUROC = `r round(max(quick_models[[1]]$results$ROC), 2)`. Not bad for one line of code. + +Now that we have our models, we can make predictions using the `predict` function. If you provide a new data frame to `predict` it will make predictions on the new data; otherwise, it will make predictions on the training data. + +```{r} +predictions <- predict(quick_models) +predictions +``` + +We get a message about when the model was trained and how well it preformed in training, and we get back a data frame that looks sort of like the original, but has a new column `predited_diabetes` that contains the model-generated probability each individual has diabetes, and contains changes that were made preparing the data for model training, e.g. missingness has been filled in and `weight_class` has been split into a series of "dummy" variables. + +We can plot how effectively the model is able to separate diabetic from non-diabetic individuals by calling the `plot` function on the output of `predict`. + +```{r} +plot(predictions) +``` + +# Data Profiling + +It is always a good idea to be aware of where there are missing values in data. The `missingness` function helps with that. In addition to looking for values R sees as missing, it looks for other values that might represent missing, such as `"NULL"`, and issues a warning if it finds any. + +```{r} +missingness(pima_diabetes) +``` + +It's good that we don't have any missingness in our ID or outcome columns. We'll see how missingness in predictors is addressed further down. + +# Data Preparation + +To get an honest picture of how well a model performs (and an accurate estimate of how well it will perform on yet-unseen data), it is wise to hide a small portion of observations from model training and assess model performance on this "validation" or "test" dataset. In fact, `healthcareai` does this automatically and repeatedly under the hood, so it's not strictly necessary, but it's still a good idea. The `split_train_test` function simplifies this, and it ensures the test dataset has proportionally similar characteristics to the training dataset. By default, 80% of observations are used for training; that proportion can be adjusted with the `p` parameter. The `seed` parameter controls randomness so that you can get the same split every time you run the code if you want strict reproducability. + +```{r} +split_data <- split_train_test(d = pima_diabetes, + outcome = diabetes, + p = .9, + seed = 84105) +``` + +`split_data` contains two data frames, named `train` and `test`. + +One of the major workhorse functions in `healthcareai` is `prep_data`. It is called under-the-hood by `machine_learn`, so you don't have to worry about these details if you don't want to, but eventually you'll want to customize how your data is prepared; this is where you do that. The helpfile `?prep_data` describes what the function does and how it can be customized. Here, let's customize preparation to scale and center numeric variables and avoid collapsing rare factor levels into "other". + +The first arguments to `prep_data` are the same as those to `machine_learn`: data frame, ignored columns, and the outcome column. Then we can specify prep details. + +```{r} +prepped_training_data <- prep_data(split_data$train, patient_id, outcome = diabetes, + center = TRUE, scale = TRUE, + collapse_rare_factors = FALSE) +``` + +The "recipe" that the above message refers to is a set of instructions for how to transform a dataset the way we just transformed our training data. Any machine learning that we do (within `healthcareai`) on `prepped_training_data` will retain that recipe and apply it before making predictions on new data. That means that when you have models making predictions in production, you don't have to figure out how to transform the data or worry about encountering missing data or new category levels. + +# Model Training + +`machine_learn` takes care of data preparation and model training for you, but if you want more precise control, `tune_models` and `flash_models` are the model-training function you're looking for. They differ in that `tune_models` searches over hyperparameters to optimize model performance, while `flash_models` trains models at set hyperparameter values. So, `tune_models` produces better models, but takes longer (approaching 10x longer at default settings). + +Let's tune only random forests (by default, k-nearest neighbors is also tuned), and to try to really optimize model performance, let's crank `tune_depth` up a little from its default value of ten. That will tune the models over more combinations of hyperparameter values in the search for the best model. + +Let's also select "PR" as our model metric. That optimizes for area under the precision-recall curve rather than the default of area under the receiver operating characteristic curve ("ROC"). This is usually a good idea when one outcome category is much more common than the other category. + +```{r} +models <- tune_models(d = prepped_training_data, + outcome = diabetes, + models = "RF", + tune_depth = 25, + metric = "PR") +``` + +We get a message saying the training may take a while because we're training so many models, but in this case it takes just about 20 seconds to train all those models. + +We can examine how the model performs across hyperparameters by plotting the model object. It looks like extratrees is a superior split rule for this model, and larger values of minimum node size tend to do better. + +```{r, fig.height = 6} +plot(models) +``` + +## Faster Model Training + +If you're feeling the need for speed, `flash_models` is the function for you. It uses fixed sets of hyperparameter values to train the models, so you still get a model customized to your data, but without burning the electricity and time to precisely optimize all the details. + +If you want to choose the hyperparameter values that `flash_models` uses, you can pass them as a list to the `hyperparameters` argument. Run `get_hyperparameter_defaults()` to see the default values and get a list you can customize. + +```{r} +flash_models(d = prepped_training_data, + outcome = diabetes, + models = "RF", + metric = "PR") +``` + +In this case we sacrificed just 0.01 AUPR versus tuning the models. In our experience, that's on the small side of typical. A good workflow is often to do all of your development using `flash_models`, and as a final step before putting a model into production, retrain the model using `tune_models`. + +# Prediction + +`predict` will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to `predict` it will make predictions on the training dataset. The predicted probabilities appear in the `predicted_diabetes` column. + +```{r} +predict(models) +``` + +To get predictions on a new dataset, pass the new data to `predict`, and it will automatically be prepared based on the recipe generated on the training data. We can plot the predictions to see how well our model is doing, and we see that it's separating diabetic from non-diabetic individuals pretty well, although there a fair number of non-diabetics with high predicted probabilities of diabetes. This may be due to optimizing for precision recall, or may indicate pre-diabetic patients. + +```{r} +test_predictions <- predict(models, split_data$test) +plot(test_predictions) +``` + +# A Regression Example + +All the examples above have been classification tasks, predicting a yes/no outcome. Here's an example of a full regression modeling pipeline on a silly problem: predicting individuals' ages. The code is very similar to classification. + +```{r} +regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age) +summary(regression_models) +``` + +Let's make a prediction on a hypothetical new patient. Note that the model handles missingness in `insulin` and a new category level in `weight_class` without a problem (but warns about it). + +```{r} +new_patient <- data.frame( + pregnancies = 0, + plasma_glucose = 80, + diastolic_bp = 55, + skinfold = 24, + insulin = NA, + weight_class = "???", + pedigree = .2, + diabetes = "N") +predict(regression_models, new_patient) +``` + diff --git a/vignettes/healthcareai.html b/inst/doc/healthcareai.html similarity index 99% rename from vignettes/healthcareai.html rename to inst/doc/healthcareai.html index 982c6e0ef..127e55f43 100644 --- a/vignettes/healthcareai.html +++ b/inst/doc/healthcareai.html @@ -313,7 +313,7 @@

    Easy Machine Learning

    #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 05:58:09 +#> Models Trained: 2018-04-02 11:37:06 #> #> Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. #> Best model: Random Forest @@ -326,7 +326,7 @@

    Easy Machine Learning

    Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

    predictions <- predict(quick_models)
     predictions
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 05:58:09
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:37:06
     #>  Performance in training: ROC = 0.84
     #>  # A tibble: 768 x 14
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -412,7 +412,7 @@ 

    Faster Model Training

    #> Performance Metric: PR #> Number of Observations: 692 #> Number of Features: 13 -#> Models Trained: 2018-04-02 05:58:33 +#> Models Trained: 2018-04-02 11:37:33 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -428,7 +428,7 @@

    Faster Model Training

    Prediction

    predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

    predict(models)
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 05:58:30
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:37:30
     #>  Performance in training: PR = 0.9
     #>  # A tibble: 692 x 15
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -459,7 +459,7 @@ 

    A Regression Example

    #> Running cross validation for Random Forest #> Running cross validation for k-Nearest Neighbors summary(regression_models) -#> Models trained: 2018-04-02 05:58:45 +#> Models trained: 2018-04-02 11:37:47 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best performance: RMSE = 9.07 @@ -505,7 +505,7 @@

    A Regression Example

    #> Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. #> weight_class: ??? #> Prepping data based on provided recipe -#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 05:58:45 +#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:37:47 #> Performance in training: RMSE = 9.07 #> # A tibble: 1 x 9 #> predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin From 2080e9ed65671956cf29a626d0a7a9c3a8c68e9e Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 11:46:53 -0600 Subject: [PATCH 2/9] Put readme plots in man/figures/ --- README.Rmd | 5 +++-- README.md | 8 ++++---- man/figures/README-plot predictions-1.png | Bin 0 -> 29676 bytes readme_files/figure-gfm/unnamed-chunk-3-1.png | Bin 29711 -> 0 bytes 4 files changed, 7 insertions(+), 6 deletions(-) create mode 100644 man/figures/README-plot predictions-1.png delete mode 100644 readme_files/figure-gfm/unnamed-chunk-3-1.png diff --git a/README.Rmd b/README.Rmd index 183e67b9e..b25ba5307 100644 --- a/README.Rmd +++ b/README.Rmd @@ -6,7 +6,8 @@ output: github_document ```{r, include = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "# >", - fig.height = 4, fig.width = 6) + fig.height = 4, fig.width = 6, + fig.path = "man/figures/README-") options(tibble.print_max = 5) library(healthcareai) ``` @@ -45,7 +46,7 @@ models Make predictions and examine predictive performance: -```{r, fig.height = 3} +```{r plot predictions, fig.height = 3} predictions <- predict(models) plot(predictions) ``` diff --git a/README.md b/README.md index 286da0392..a6fe403da 100644 --- a/README.md +++ b/README.md @@ -58,15 +58,15 @@ models # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 11:02:25 +# > Models Trained: 2018-04-02 11:45:57 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest # > ROC = 0.85 # > Optimal hyperparameter values: -# > mtry = 3 +# > mtry = 4 # > splitrule = extratrees -# > min.node.size = 19 +# > min.node.size = 18 ``` Make predictions and examine predictive performance: @@ -76,7 +76,7 @@ predictions <- predict(models) plot(predictions) ``` -![](readme_files/figure-gfm/unnamed-chunk-3-1.png) +![](man/figures/README-plot%20predictions-1.png) ## Learn More diff --git a/man/figures/README-plot predictions-1.png b/man/figures/README-plot predictions-1.png new file mode 100644 index 0000000000000000000000000000000000000000..ebebf4d9e59858f4ef0785ee87ff89714e5b9ea6 GIT binary patch literal 29676 zcmYhiWk4NE(=`ekx8MZV;O;KL-Q6WwRIxWQF_kd2HFdN!QI-$}1LKO0R@1YjMqgKxKQYxU^0GiZV-h_1(hM<` zK>b6I;z4B5aT+gcnq_Ovn(3MMb>?}N*IA4CIn+T*nPzf41~^uF75zt+EvL}fFn-#& z;Lx0p(775)1hjgkvLFrx1h)pAb&+3Q28*++Z`qYi;slt)4n)RvdnE{AvEUcQY9p@T zo=@>af=Akfcg6=z%tC#Pp3R#k^Sw?2zR{LuJ9JyqVNhx+R$(b9v7`MD;)u`8Kj#c` z7 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zUgx{X1a0cSHdy0olG}2R!~-lX^efUr6{B(bFd8H=`;}0}$wAC|^%GiDkueR@A7UUy zILEyi?g1GGxwN?7hcr?Ie@gg)_A2s1%T`sHpjaFUNUAbhNf1qmV^A>`lX5FIW zL5LEux+Z@bAB`tB_I8aG{R>Iz7yQ89s!a<7rDCI6+vGKt!T2@CCtXP^7$1btZY4kc7$B6OB zdTm=rXN7826kXkN@yjs%%eFS1o{B&D&Yxz<1L`~u78-SlbXv`2bbcN&+$kK%PF%$dLb-;&KQ;OB}mhHE331>f2wfsa?@LVY;ai?JYb1ahb0J_ zf@M#^C!a$8_W)R^v%FW+M=7N-|9l#J1^5^{!dJLt^lwC#u&;z2loX*19$q6rulr5a zbMpVZ6eeDTnuegso~51k&s+X^XALqq30o}3AM@7#{cr*Okr?yp)?8P=eJ>pNdnKbR JT`p-H^gl5WkHP=| From 9134693f7fb365d623370fab78e357e24765b857 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 15:43:12 -0600 Subject: [PATCH 3/9] remove vignettes from inst, closes #1011 --- docs/dev/articles/healthcareai.html | 12 +- docs/dev/index.html | 8 +- .../figures/README-plot predictions-1.png | Bin 0 -> 70796 bytes docs/dev/reference/flash_models.html | 4 +- docs/dev/reference/machine_learn.html | 6 +- docs/dev/reference/predict.model_list.html | 2 +- inst/doc/healthcareai.R | 78 --- inst/doc/healthcareai.Rmd | 174 ------ inst/doc/healthcareai.html | 531 ------------------ man/figures/README-plot predictions-1.png | Bin 29676 -> 70796 bytes 10 files changed, 16 insertions(+), 799 deletions(-) create mode 100644 docs/dev/reference/figures/README-plot predictions-1.png delete mode 100644 inst/doc/healthcareai.R delete mode 100644 inst/doc/healthcareai.Rmd delete mode 100644 inst/doc/healthcareai.html diff --git a/docs/dev/articles/healthcareai.html b/docs/dev/articles/healthcareai.html index 124a82ebc..5aff5cf7b 100644 --- a/docs/dev/articles/healthcareai.html +++ b/docs/dev/articles/healthcareai.html @@ -133,7 +133,7 @@

    #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 11:41:37 +#> Models Trained: 2018-04-02 15:40:56 #> #> Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. #> Best model: Random Forest @@ -146,7 +146,7 @@

    Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

    predictions <- predict(quick_models)
     predictions
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:41:37
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:40:56
     #>  Performance in training: ROC = 0.84
     #>  # A tibble: 768 x 14
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -236,7 +236,7 @@ 

    #> Performance Metric: PR #> Number of Observations: 692 #> Number of Features: 13 -#> Models Trained: 2018-04-02 11:42:03 +#> Models Trained: 2018-04-02 15:41:20 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -253,7 +253,7 @@

    Prediction

    predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

    predict(models)
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:42:00
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:41:18
     #>  Performance in training: PR = 0.9
     #>  # A tibble: 692 x 15
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -285,7 +285,7 @@ 

    #> Running cross validation for Random Forest #> Running cross validation for k-Nearest Neighbors summary(regression_models) -#> Models trained: 2018-04-02 11:42:16 +#> Models trained: 2018-04-02 15:41:33 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best performance: RMSE = 9.07 @@ -331,7 +331,7 @@

    #> Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. #> weight_class: ??? #> Prepping data based on provided recipe -#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:42:16 +#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 15:41:33 #> Performance in training: RMSE = 9.07 #> # A tibble: 1 x 9 #> predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin diff --git a/docs/dev/index.html b/docs/dev/index.html index ef3b7e5b0..8c113f9d6 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -127,19 +127,19 @@

    # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 11:40:18 +# > Models Trained: 2018-04-02 15:39:38 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest # > ROC = 0.85 # > Optimal hyperparameter values: -# > mtry = 3 +# > mtry = 5 # > splitrule = extratrees -# > min.node.size = 6

    +# > min.node.size = 19

    Make predictions and examine predictive performance:

    -

    +

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z0L@5iKiU}YZ4Zp6tW+*3cAz7>gCW=Sgz9zmVW)|5~#50BoH(ZXl)*K7q(f}os=BttcwsClD_OE+5-wez7O zNge7;bqhP`C{LZ>!|l@I0q<*rSv))LWBCsdGY@V+);&dvvm6|q8_{|&;1gI$S`Zlf zJLCFaWd-~dev5SpPXa&6)O|9INfw6G^Ja}%u(EAn85h#=GbN_sT6;Kd$0wnBZ|XOffv>6e zJ&xbmy#~1ey3STbX5aH@D-$5^dBhaZa?DdL^tHQ*hJNtg5MU@Nayt*_7oir=+=L=h z7EVzC2?71Kq4FgY9a;+37uUW)tiC-ClH&bVt^BTyGC(xeT zA~JVwOZI-#)J!mbWh+3X!Fm1H9l-4Pi%lJko}SJN7Vl;KYvn*&k^mFGo00EfKj~LJ zqycBE+`l#MIv`KZmQoi~%0>Ux(nJVi3VpH3!CZ5P_G@!-`$&~+nAK{VO-qYZ$X091%|X$ z=I^IR;RvVvor~*4={{pi8 Bg}(p* literal 0 HcmV?d00001 diff --git a/docs/dev/reference/flash_models.html b/docs/dev/reference/flash_models.html index a3a3742cf..e281aead7 100644 --- a/docs/dev/reference/flash_models.html +++ b/docs/dev/reference/flash_models.html @@ -207,7 +207,7 @@

    Examp #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 11:40:29 +#> Models Trained: 2018-04-02 15:39:49 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -232,7 +232,7 @@

    Examp kernel = "gaussian" ) ) - )

    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 11:40:32 + )
    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 15:39:51 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best algorithm: Random Forest with ROC = 0.84 diff --git a/docs/dev/reference/machine_learn.html b/docs/dev/reference/machine_learn.html index 4211befb1..f87881a5d 100644 --- a/docs/dev/reference/machine_learn.html +++ b/docs/dev/reference/machine_learn.html @@ -201,7 +201,7 @@

    Examp # Clean and prep the data, tune algorithms over hyperparameter values to predict diabetes diabetes_models <- machine_learn(training_data, outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make predictions (predicted probability of diabetes) on test data -predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:40:39 +predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:39:59 #> Performance in training: ROC = 0.75
    #> # A tibble: 10 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> @@ -222,7 +222,7 @@

    Examp # Predict numeric outcomes simply by specifying the name of the outcome variable age_model <- machine_learn(training_data, outcome = age)

    #> Training new data prep recipe
    #> age looks numeric, so training regression algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # If new data isn't specifed, get predictions on training data. Plot predictions -predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 11:40:43 +predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 15:40:02 #> Performance in training: RMSE = 9.88
    #> # A tibble: 50 x 17 #> age predicted_age patient_id pregnancies plasma_glucose diastolic_bp #> * <int> <dbl> <int> <int> <int> <dbl> @@ -251,7 +251,7 @@

    Examp #> Performance Metric: ROC #> Number of Observations: 50 #> Number of Features: 13 -#> Models Trained: 2018-04-02 11:40:44 +#> Models Trained: 2018-04-02 15:40:04 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest diff --git a/docs/dev/reference/predict.model_list.html b/docs/dev/reference/predict.model_list.html index 6f8e93ea0..f23a582cb 100644 --- a/docs/dev/reference/predict.model_list.html +++ b/docs/dev/reference/predict.model_list.html @@ -189,7 +189,7 @@

    Examp models <- machine_learn(pima_diabetes[1:50, ], outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make prediction on the next 20 rows. This uses the best-performing model from # tuning cross validation, and it also prepares the new data in the same way as # the training data was prepared. -predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 11:41:05 +predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:40:24 #> Performance in training: ROC = 0.73
    #> # A tibble: 20 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> diff --git a/inst/doc/healthcareai.R b/inst/doc/healthcareai.R deleted file mode 100644 index c4030f0d1..000000000 --- a/inst/doc/healthcareai.R +++ /dev/null @@ -1,78 +0,0 @@ -## ----setup, include=FALSE------------------------------------------------ -set.seed(43170) -knitr::opts_chunk$set(echo = TRUE, results = "hold", collapse = TRUE, - comment = "#> ") -options(tibble.print_min = 5, tibble.print_max = 5) - -## ------------------------------------------------------------------------ -library(healthcareai) - -## ------------------------------------------------------------------------ -str(pima_diabetes) - -## ------------------------------------------------------------------------ -quick_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes) - -## ------------------------------------------------------------------------ -quick_models - -## ------------------------------------------------------------------------ -predictions <- predict(quick_models) -predictions - -## ------------------------------------------------------------------------ -plot(predictions) - -## ------------------------------------------------------------------------ -missingness(pima_diabetes) - -## ------------------------------------------------------------------------ -split_data <- split_train_test(d = pima_diabetes, - outcome = diabetes, - p = .9, - seed = 84105) - -## ------------------------------------------------------------------------ -prepped_training_data <- prep_data(split_data$train, patient_id, outcome = diabetes, - center = TRUE, scale = TRUE, - collapse_rare_factors = FALSE) - -## ------------------------------------------------------------------------ -models <- tune_models(d = prepped_training_data, - outcome = diabetes, - models = "RF", - tune_depth = 25, - metric = "PR") - -## ---- fig.height = 6----------------------------------------------------- -plot(models) - -## ------------------------------------------------------------------------ -flash_models(d = prepped_training_data, - outcome = diabetes, - models = "RF", - metric = "PR") - -## ------------------------------------------------------------------------ -predict(models) - -## ------------------------------------------------------------------------ -test_predictions <- predict(models, split_data$test) -plot(test_predictions) - -## ------------------------------------------------------------------------ -regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age) -summary(regression_models) - -## ------------------------------------------------------------------------ -new_patient <- data.frame( - pregnancies = 0, - plasma_glucose = 80, - diastolic_bp = 55, - skinfold = 24, - insulin = NA, - weight_class = "???", - pedigree = .2, - diabetes = "N") -predict(regression_models, new_patient) - diff --git a/inst/doc/healthcareai.Rmd b/inst/doc/healthcareai.Rmd deleted file mode 100644 index 802c814f4..000000000 --- a/inst/doc/healthcareai.Rmd +++ /dev/null @@ -1,174 +0,0 @@ ---- -title: "Getting Started with healthcareai" -output: rmarkdown::html_vignette -vignette: > - %\VignetteIndexEntry{Getting Started with healthcareai} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r setup, include=FALSE} -set.seed(43170) -knitr::opts_chunk$set(echo = TRUE, results = "hold", collapse = TRUE, - comment = "#> ") -options(tibble.print_min = 5, tibble.print_max = 5) -``` - -First we attach the healthcareai R package to make its functions available. If your package version is less than 2.0, none of the code here will work. You can check the package version with `packageVersion("healthcareai")`, and you can get the latest, cutting-edge development version with `install.packages("remotes"); remotes::install_github("HealthCatalyst/healthcareai-r")`. - -```{r} -library(healthcareai) -``` - -`healthcareai` comes with a built in dataset documenting diabetes among adult Pima females. Once you attach the package, the dataset is available in the variable `pima_diabetes`. Let's take a look at the data with the `str` function. There are 768 records in 10 variables including one identifier column, several nominal variables, and substantial missingness (represented in R by `NA`). - -```{r} -str(pima_diabetes) -``` - -# Easy Machine Learning - -If you don't want to fuss with details any more than necessary, `machine_learn` is the function for you. It makes it as easy as possible to implement machine learning models by putting all the detais in the background so that you don't have to worry about them. Of course it might be wise to worry about them, and we'll get to how to do that further down, but for now, you can automatically take care of problems in the data, do basic feature engineering, and tune multiple machine learning models using cross validation with `machine_learn`. - -`machine_learn` always gets the name of the data frame, then any columns that should not be used by the model (uninformative columns, such as IDs), then the variable to be predicted with `outcome =`. If you want `machine_learn` to run faster, you can have that---at the expense of a bit of predictive power---by setting its `tune` argument to `FALSE`. - -```{r} -quick_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes) -``` - -`machine_learn` has told us that it has created a recipe for data preparation (this allows us to do exactly the same data cleaning and feature engineering when you want predictions on a new dataset), is ignoring `patient_id` when tuning models as we told it to, is training classification algorithms because the outcome variable `diabetes` is categorical, and has executed cross validation for two machine learning models: random forests, and k-nearest neighbors. Let's see what the models look like. - -```{r} -quick_models -``` - -Everything looks as expected, and the best model is is a random forest that achives performance of AUROC = `r round(max(quick_models[[1]]$results$ROC), 2)`. Not bad for one line of code. - -Now that we have our models, we can make predictions using the `predict` function. If you provide a new data frame to `predict` it will make predictions on the new data; otherwise, it will make predictions on the training data. - -```{r} -predictions <- predict(quick_models) -predictions -``` - -We get a message about when the model was trained and how well it preformed in training, and we get back a data frame that looks sort of like the original, but has a new column `predited_diabetes` that contains the model-generated probability each individual has diabetes, and contains changes that were made preparing the data for model training, e.g. missingness has been filled in and `weight_class` has been split into a series of "dummy" variables. - -We can plot how effectively the model is able to separate diabetic from non-diabetic individuals by calling the `plot` function on the output of `predict`. - -```{r} -plot(predictions) -``` - -# Data Profiling - -It is always a good idea to be aware of where there are missing values in data. The `missingness` function helps with that. In addition to looking for values R sees as missing, it looks for other values that might represent missing, such as `"NULL"`, and issues a warning if it finds any. - -```{r} -missingness(pima_diabetes) -``` - -It's good that we don't have any missingness in our ID or outcome columns. We'll see how missingness in predictors is addressed further down. - -# Data Preparation - -To get an honest picture of how well a model performs (and an accurate estimate of how well it will perform on yet-unseen data), it is wise to hide a small portion of observations from model training and assess model performance on this "validation" or "test" dataset. In fact, `healthcareai` does this automatically and repeatedly under the hood, so it's not strictly necessary, but it's still a good idea. The `split_train_test` function simplifies this, and it ensures the test dataset has proportionally similar characteristics to the training dataset. By default, 80% of observations are used for training; that proportion can be adjusted with the `p` parameter. The `seed` parameter controls randomness so that you can get the same split every time you run the code if you want strict reproducability. - -```{r} -split_data <- split_train_test(d = pima_diabetes, - outcome = diabetes, - p = .9, - seed = 84105) -``` - -`split_data` contains two data frames, named `train` and `test`. - -One of the major workhorse functions in `healthcareai` is `prep_data`. It is called under-the-hood by `machine_learn`, so you don't have to worry about these details if you don't want to, but eventually you'll want to customize how your data is prepared; this is where you do that. The helpfile `?prep_data` describes what the function does and how it can be customized. Here, let's customize preparation to scale and center numeric variables and avoid collapsing rare factor levels into "other". - -The first arguments to `prep_data` are the same as those to `machine_learn`: data frame, ignored columns, and the outcome column. Then we can specify prep details. - -```{r} -prepped_training_data <- prep_data(split_data$train, patient_id, outcome = diabetes, - center = TRUE, scale = TRUE, - collapse_rare_factors = FALSE) -``` - -The "recipe" that the above message refers to is a set of instructions for how to transform a dataset the way we just transformed our training data. Any machine learning that we do (within `healthcareai`) on `prepped_training_data` will retain that recipe and apply it before making predictions on new data. That means that when you have models making predictions in production, you don't have to figure out how to transform the data or worry about encountering missing data or new category levels. - -# Model Training - -`machine_learn` takes care of data preparation and model training for you, but if you want more precise control, `tune_models` and `flash_models` are the model-training function you're looking for. They differ in that `tune_models` searches over hyperparameters to optimize model performance, while `flash_models` trains models at set hyperparameter values. So, `tune_models` produces better models, but takes longer (approaching 10x longer at default settings). - -Let's tune only random forests (by default, k-nearest neighbors is also tuned), and to try to really optimize model performance, let's crank `tune_depth` up a little from its default value of ten. That will tune the models over more combinations of hyperparameter values in the search for the best model. - -Let's also select "PR" as our model metric. That optimizes for area under the precision-recall curve rather than the default of area under the receiver operating characteristic curve ("ROC"). This is usually a good idea when one outcome category is much more common than the other category. - -```{r} -models <- tune_models(d = prepped_training_data, - outcome = diabetes, - models = "RF", - tune_depth = 25, - metric = "PR") -``` - -We get a message saying the training may take a while because we're training so many models, but in this case it takes just about 20 seconds to train all those models. - -We can examine how the model performs across hyperparameters by plotting the model object. It looks like extratrees is a superior split rule for this model, and larger values of minimum node size tend to do better. - -```{r, fig.height = 6} -plot(models) -``` - -## Faster Model Training - -If you're feeling the need for speed, `flash_models` is the function for you. It uses fixed sets of hyperparameter values to train the models, so you still get a model customized to your data, but without burning the electricity and time to precisely optimize all the details. - -If you want to choose the hyperparameter values that `flash_models` uses, you can pass them as a list to the `hyperparameters` argument. Run `get_hyperparameter_defaults()` to see the default values and get a list you can customize. - -```{r} -flash_models(d = prepped_training_data, - outcome = diabetes, - models = "RF", - metric = "PR") -``` - -In this case we sacrificed just 0.01 AUPR versus tuning the models. In our experience, that's on the small side of typical. A good workflow is often to do all of your development using `flash_models`, and as a final step before putting a model into production, retrain the model using `tune_models`. - -# Prediction - -`predict` will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to `predict` it will make predictions on the training dataset. The predicted probabilities appear in the `predicted_diabetes` column. - -```{r} -predict(models) -``` - -To get predictions on a new dataset, pass the new data to `predict`, and it will automatically be prepared based on the recipe generated on the training data. We can plot the predictions to see how well our model is doing, and we see that it's separating diabetic from non-diabetic individuals pretty well, although there a fair number of non-diabetics with high predicted probabilities of diabetes. This may be due to optimizing for precision recall, or may indicate pre-diabetic patients. - -```{r} -test_predictions <- predict(models, split_data$test) -plot(test_predictions) -``` - -# A Regression Example - -All the examples above have been classification tasks, predicting a yes/no outcome. Here's an example of a full regression modeling pipeline on a silly problem: predicting individuals' ages. The code is very similar to classification. - -```{r} -regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age) -summary(regression_models) -``` - -Let's make a prediction on a hypothetical new patient. Note that the model handles missingness in `insulin` and a new category level in `weight_class` without a problem (but warns about it). - -```{r} -new_patient <- data.frame( - pregnancies = 0, - plasma_glucose = 80, - diastolic_bp = 55, - skinfold = 24, - insulin = NA, - weight_class = "???", - pedigree = .2, - diabetes = "N") -predict(regression_models, new_patient) -``` - diff --git a/inst/doc/healthcareai.html b/inst/doc/healthcareai.html deleted file mode 100644 index 127e55f43..000000000 --- a/inst/doc/healthcareai.html +++ /dev/null @@ -1,531 +0,0 @@ - - - - - - - - - - - - - - -Getting Started with healthcareai - - - - - - - - - - - - - - - - - -

    Getting Started with healthcareai

    - - - -

    First we attach the healthcareai R package to make its functions available. If your package version is less than 2.0, none of the code here will work. You can check the package version with packageVersion("healthcareai"), and you can get the latest, cutting-edge development version with install.packages("remotes"); remotes::install_github("HealthCatalyst/healthcareai-r").

    - -

    healthcareai comes with a built in dataset documenting diabetes among adult Pima females. Once you attach the package, the dataset is available in the variable pima_diabetes. Let’s take a look at the data with the str function. There are 768 records in 10 variables including one identifier column, several nominal variables, and substantial missingness (represented in R by NA).

    - -
    -

    Easy Machine Learning

    -

    If you don’t want to fuss with details any more than necessary, machine_learn is the function for you. It makes it as easy as possible to implement machine learning models by putting all the detais in the background so that you don’t have to worry about them. Of course it might be wise to worry about them, and we’ll get to how to do that further down, but for now, you can automatically take care of problems in the data, do basic feature engineering, and tune multiple machine learning models using cross validation with machine_learn.

    -

    machine_learn always gets the name of the data frame, then any columns that should not be used by the model (uninformative columns, such as IDs), then the variable to be predicted with outcome =. If you want machine_learn to run faster, you can have that—at the expense of a bit of predictive power—by setting its tune argument to FALSE.

    - -

    machine_learn has told us that it has created a recipe for data preparation (this allows us to do exactly the same data cleaning and feature engineering when you want predictions on a new dataset), is ignoring patient_id when tuning models as we told it to, is training classification algorithms because the outcome variable diabetes is categorical, and has executed cross validation for two machine learning models: random forests, and k-nearest neighbors. Let’s see what the models look like.

    - -

    Everything looks as expected, and the best model is is a random forest that achives performance of AUROC = 0.84. Not bad for one line of code.

    -

    Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

    - -

    We get a message about when the model was trained and how well it preformed in training, and we get back a data frame that looks sort of like the original, but has a new column predited_diabetes that contains the model-generated probability each individual has diabetes, and contains changes that were made preparing the data for model training, e.g. missingness has been filled in and weight_class has been split into a series of “dummy” variables.

    -

    We can plot how effectively the model is able to separate diabetic from non-diabetic individuals by calling the plot function on the output of predict.

    - -

    -
    -
    -

    Data Profiling

    -

    It is always a good idea to be aware of where there are missing values in data. The missingness function helps with that. In addition to looking for values R sees as missing, it looks for other values that might represent missing, such as "NULL", and issues a warning if it finds any.

    - -

    It’s good that we don’t have any missingness in our ID or outcome columns. We’ll see how missingness in predictors is addressed further down.

    -
    -
    -

    Data Preparation

    -

    To get an honest picture of how well a model performs (and an accurate estimate of how well it will perform on yet-unseen data), it is wise to hide a small portion of observations from model training and assess model performance on this “validation” or “test” dataset. In fact, healthcareai does this automatically and repeatedly under the hood, so it’s not strictly necessary, but it’s still a good idea. The split_train_test function simplifies this, and it ensures the test dataset has proportionally similar characteristics to the training dataset. By default, 80% of observations are used for training; that proportion can be adjusted with the p parameter. The seed parameter controls randomness so that you can get the same split every time you run the code if you want strict reproducability.

    - -

    split_data contains two data frames, named train and test.

    -

    One of the major workhorse functions in healthcareai is prep_data. It is called under-the-hood by machine_learn, so you don’t have to worry about these details if you don’t want to, but eventually you’ll want to customize how your data is prepared; this is where you do that. The helpfile ?prep_data describes what the function does and how it can be customized. Here, let’s customize preparation to scale and center numeric variables and avoid collapsing rare factor levels into “other”.

    -

    The first arguments to prep_data are the same as those to machine_learn: data frame, ignored columns, and the outcome column. Then we can specify prep details.

    - -

    The “recipe” that the above message refers to is a set of instructions for how to transform a dataset the way we just transformed our training data. Any machine learning that we do (within healthcareai) on prepped_training_data will retain that recipe and apply it before making predictions on new data. That means that when you have models making predictions in production, you don’t have to figure out how to transform the data or worry about encountering missing data or new category levels.

    -
    -
    -

    Model Training

    -

    machine_learn takes care of data preparation and model training for you, but if you want more precise control, tune_models and flash_models are the model-training function you’re looking for. They differ in that tune_models searches over hyperparameters to optimize model performance, while flash_models trains models at set hyperparameter values. So, tune_models produces better models, but takes longer (approaching 10x longer at default settings).

    -

    Let’s tune only random forests (by default, k-nearest neighbors is also tuned), and to try to really optimize model performance, let’s crank tune_depth up a little from its default value of ten. That will tune the models over more combinations of hyperparameter values in the search for the best model.

    -

    Let’s also select “PR” as our model metric. That optimizes for area under the precision-recall curve rather than the default of area under the receiver operating characteristic curve (“ROC”). This is usually a good idea when one outcome category is much more common than the other category.

    - -

    We get a message saying the training may take a while because we’re training so many models, but in this case it takes just about 20 seconds to train all those models.

    -

    We can examine how the model performs across hyperparameters by plotting the model object. It looks like extratrees is a superior split rule for this model, and larger values of minimum node size tend to do better.

    - -

    -
    -

    Faster Model Training

    -

    If you’re feeling the need for speed, flash_models is the function for you. It uses fixed sets of hyperparameter values to train the models, so you still get a model customized to your data, but without burning the electricity and time to precisely optimize all the details.

    -

    If you want to choose the hyperparameter values that flash_models uses, you can pass them as a list to the hyperparameters argument. Run get_hyperparameter_defaults() to see the default values and get a list you can customize.

    - -

    In this case we sacrificed just 0.01 AUPR versus tuning the models. In our experience, that’s on the small side of typical. A good workflow is often to do all of your development using flash_models, and as a final step before putting a model into production, retrain the model using tune_models.

    -
    -
    -
    -

    Prediction

    -

    predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

    - -

    To get predictions on a new dataset, pass the new data to predict, and it will automatically be prepared based on the recipe generated on the training data. We can plot the predictions to see how well our model is doing, and we see that it’s separating diabetic from non-diabetic individuals pretty well, although there a fair number of non-diabetics with high predicted probabilities of diabetes. This may be due to optimizing for precision recall, or may indicate pre-diabetic patients.

    - -

    -
    -
    -

    A Regression Example

    -

    All the examples above have been classification tasks, predicting a yes/no outcome. Here’s an example of a full regression modeling pipeline on a silly problem: predicting individuals’ ages. The code is very similar to classification.

    -
    regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age)
    -#>  Training new data prep recipe
    -#>  Variable(s) ignored in prep_data won't be used to tune models: patient_id
    -#>  age looks numeric, so training regression algorithms.
    -#>  Running cross validation for Random Forest
    -#>  Running cross validation for k-Nearest Neighbors
    -summary(regression_models)
    -#>  Models trained: 2018-04-02 11:37:47
    -#>  
    -#>  Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values.
    -#>  Best performance: RMSE = 9.07
    -#>  By Random Forest with hyperparameters:
    -#>    mtry = 10
    -#>    splitrule = extratrees
    -#>    min.node.size = 10
    -#>  
    -#>  Out-of-fold performance of all trained models:
    -#>  
    -#>  $`Random Forest`
    -#>  # A tibble: 10 x 9
    -#>    min.node.size  mtry splitrule   RMSE Rsquared   MAE RMSESD RsquaredSD
    -#>  *         <int> <int> <fct>      <dbl>    <dbl> <dbl>  <dbl>      <dbl>
    -#>  1            10    10 extratrees  9.07    0.404  6.43  0.640     0.0358
    -#>  2             8    11 extratrees  9.09    0.402  6.43  0.626     0.0396
    -#>  3            12     5 extratrees  9.13    0.405  6.56  0.666     0.0272
    -#>  4            10    13 variance    9.33    0.376  6.60  0.633     0.0358
    -#>  5             7    10 variance    9.34    0.374  6.61  0.583     0.0303
    -#>  # ... with 5 more rows, and 1 more variable: MAESD <dbl>
    -#>  
    -#>  $`k-Nearest Neighbors`
    -#>  # A tibble: 10 x 9
    -#>     kmax distance kernel       RMSE Rsquared   MAE RMSESD RsquaredSD MAESD
    -#>  * <dbl>    <dbl> <fct>       <dbl>    <dbl> <dbl>  <dbl>      <dbl> <dbl>
    -#>  1   16.    2.60  inv          9.44    0.363  6.65  0.811     0.0649 0.551
    -#>  2   14.    1.73  gaussian     9.44    0.361  6.66  0.717     0.0593 0.452
    -#>  3   13.    1.58  triangular   9.49    0.355  6.66  0.764     0.0697 0.461
    -#>  4   10.    0.933 rectangular  9.55    0.346  6.79  0.637     0.0438 0.412
    -#>  5    6.    1.68  inv          9.64    0.340  6.74  0.723     0.0677 0.465
    -#>  # ... with 5 more rows
    -

    Let’s make a prediction on a hypothetical new patient. Note that the model handles missingness in insulin and a new category level in weight_class without a problem (but warns about it).

    - -
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a/README.md b/README.md index 286da0392..571efdf56 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ - + # healthcareai @@ -58,15 +58,15 @@ models # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 11:02:25 +# > Models Trained: 2018-04-02 15:48:43 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest -# > ROC = 0.85 +# > ROC = 0.84 # > Optimal hyperparameter values: -# > mtry = 3 +# > mtry = 4 # > splitrule = extratrees -# > min.node.size = 19 +# > min.node.size = 15 ``` Make predictions and examine predictive performance: diff --git a/readme.Rmd b/readme.Rmd index 183e67b9e..fff4d7255 100644 --- a/readme.Rmd +++ b/readme.Rmd @@ -2,7 +2,7 @@ output: github_document --- - + ```{r, include = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "# >", diff --git a/readme_files/figure-gfm/unnamed-chunk-3-1.png 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zUDZ{&;E=zgY3u(ShNETQV&F}I6ScIga+nxPsyy<1nfq1_Ray>Na8K15WP_nzY*y=# zvdlG*xXga1GZ~5hN-{CKr?xQPqk`g2WZKsUU(E#ZjPyTlG$Y@`mI7XNE`>vs&IOE) z$D%PX^&HE_1Ir%mOlr|s_<8+C$?NPMj`i>-`POdAzpCEVQU$(Aql}z=pgdwpdSJP+ z54@r}&lMk)+Q0l~_UeZCKkdHpCa|qi869^a2cd0G5DfJ%WE0uU5C*q}psm`?nF^Ci zIO7h#1S_Yj^s=4$@OcWc8T+37^16Wml40q=Ep6NRoFUeSt*r*#6~FV{-cQp8)p_sC zH5yVHe6dzE__oKE|N4hpQ}P!>bgzYMf|EJ)hp>8za&hD5d@0cI8^4RUde{L$a}0;M zV+Sy`%3B4G^fQdC^83>}MXD!r!BEB7C+W{Ky}A4YY4Z zy0DY@jms!qz>*&!BMKXX7VfG#h!ddyb28|q>ZNG-)=RxA)pHR+Eatcr4?*VEQ(2t} z{?kN5hm{r3$f)aS)jit(yc8i*gqx0`%bTg6{?A+fd1nnaI12|tm#?$- isDl~*_vixFJ!#IRUo)M3PCaPg?~$U0Lb;rI$o~M6xZ?Ey From 6b546f637e8980a342e54490962025c33335adbb Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 15:54:33 -0600 Subject: [PATCH 5/9] In split_train_test p -> percent_train. Closes #1001 --- R/split_train_test.R | 7 ++++--- man/split_train_test.Rd | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/R/split_train_test.R b/R/split_train_test.R index d671210cd..6a6c48864 100644 --- a/R/split_train_test.R +++ b/R/split_train_test.R @@ -3,7 +3,7 @@ #' @param d Data frame #' @param outcome Target column, unquoted. Split will be stratified across this #' variable -#' @param p Proportion of rows in d to put into training. Default is 0.8 +#' @param percent_train Proportion of rows in d to put into training. Default is 0.8 #' @param seed Optional, if provided the function will return the same split #' each time it is called #' @@ -14,7 +14,7 @@ #' #' @examples #' split_train_test(mtcars, am, .9) -split_train_test <- function(d, outcome, p = .8, seed) { +split_train_test <- function(d, outcome, percent_train = .8, seed) { outcome <- rlang::enquo(outcome) if (rlang::quo_is_missing(outcome)) stop("You must provide an outcome variable to tune_models.") @@ -23,6 +23,7 @@ split_train_test <- function(d, outcome, p = .8, seed) { stop(outcome_chr, " isn't a column in d.") if (!missing(seed)) set.seed(seed) - train_rows <- caret::createDataPartition(dplyr::pull(d, !!outcome), p = p)[[1]] + train_rows <- caret::createDataPartition(dplyr::pull(d, !!outcome), + p = percent_train)[[1]] list(train = d[train_rows, ], test = d[-train_rows, ]) } diff --git a/man/split_train_test.Rd b/man/split_train_test.Rd index 1fa60ff67..444fafda6 100644 --- a/man/split_train_test.Rd +++ b/man/split_train_test.Rd @@ -4,7 +4,7 @@ \alias{split_train_test} \title{Split data into training and test data frames} \usage{ -split_train_test(d, outcome, p = 0.8, seed) +split_train_test(d, outcome, percent_train = 0.8, seed) } \arguments{ \item{d}{Data frame} @@ -12,7 +12,7 @@ split_train_test(d, outcome, p = 0.8, seed) \item{outcome}{Target column, unquoted. Split will be stratified across this variable} -\item{p}{Proportion of rows in d to put into training. Default is 0.8} +\item{percent_train}{Proportion of rows in d to put into training. Default is 0.8} \item{seed}{Optional, if provided the function will return the same split each time it is called} From c146f130c12da376812d26201265db12ffffe303 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 16:03:23 -0600 Subject: [PATCH 6/9] remove spaces from code chunk name for filepath compatibility --- README.Rmd | 2 +- README.md | 8 +- docs/dev/CHANGELOG.html | 298 ------------------ docs/dev/articles/healthcareai.html | 12 +- docs/dev/index.html | 8 +- .../figure-html/unnamed-chunk-3-1.png | Bin 70036 -> 0 bytes .../figures/README-plot_predictions-1.png | Bin 0 -> 70655 bytes docs/dev/reference/flash_models.html | 4 +- docs/dev/reference/hcai_impute.html | 13 +- docs/dev/reference/machine_learn.html | 6 +- docs/dev/reference/predict.model_list.html | 2 +- man/figures/README-plot_predictions-1.png | Bin 0 -> 70655 bytes 12 files changed, 23 insertions(+), 330 deletions(-) delete mode 100644 docs/dev/CHANGELOG.html delete mode 100644 docs/dev/index_files/figure-html/unnamed-chunk-3-1.png create mode 100644 docs/dev/reference/figures/README-plot_predictions-1.png create mode 100644 man/figures/README-plot_predictions-1.png diff --git a/README.Rmd b/README.Rmd index b25ba5307..9f33bc610 100644 --- a/README.Rmd +++ b/README.Rmd @@ -46,7 +46,7 @@ models Make predictions and examine predictive performance: -```{r plot predictions, fig.height = 3} +```{r plot_predictions, fig.height = 3} predictions <- predict(models) plot(predictions) ``` diff --git a/README.md b/README.md index a6fe403da..c6bd10ebe 100644 --- a/README.md +++ b/README.md @@ -58,15 +58,15 @@ models # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 11:45:57 +# > Models Trained: 2018-04-02 15:57:38 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest # > ROC = 0.85 # > Optimal hyperparameter values: -# > mtry = 4 +# > mtry = 6 # > splitrule = extratrees -# > min.node.size = 18 +# > min.node.size = 13 ``` Make predictions and examine predictive performance: @@ -76,7 +76,7 @@ predictions <- predict(models) plot(predictions) ``` -![](man/figures/README-plot%20predictions-1.png) +![](man/figures/README-plot_predictions-1.png) ## Learn More diff --git a/docs/dev/CHANGELOG.html b/docs/dev/CHANGELOG.html deleted file mode 100644 index 036d78230..000000000 --- a/docs/dev/CHANGELOG.html +++ /dev/null @@ -1,298 +0,0 @@ - - - - - - - - -Change Log • healthcareai - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    All notable changes to this project will be documented in this file.

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    The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

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    -[2.0.0] - 2018-02-01

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    Major, breaking changes. R6 is out; S3 is in.

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    -[1.2.0] - 2017-10-19

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    • -Limone – a lime-like model interpretation tool. -
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      • Called via getProcessVariablesDf -
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    -[1.1.0] - 2017-10-11

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    • Deploy now saves information about the model and deployment as an attribute of the output dataframe. This information is written to a log file in the working directory.
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    • -skip_on_not_appveyor will skip a unit test unless it’s being run on Appveyor.
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    -Changed

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    • Unit tests involving MSSQL now only run on Appveyor.
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    -[1.0.0] - 2017-08-02

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    • Multiclass functionality with XGBoost is supported using XGBoostDevelopment and XGBoostDeployment.
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    • K-means clustering is supported using KmeansClustering.
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    • -findVariaion will return groups with the highest variation of a chosen target measure within a data set.
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    -[0.1.12] - 2017-05-08

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    -0.1.11 - 2017-03-02

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    Site built with pkgdown.

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    #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 15:40:56 +#> Models Trained: 2018-04-02 16:01:34 #> #> Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. #> Best model: Random Forest @@ -146,7 +146,7 @@

    Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

    predictions <- predict(quick_models)
     predictions
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:40:56
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:01:34
     #>  Performance in training: ROC = 0.84
     #>  # A tibble: 768 x 14
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -236,7 +236,7 @@ 

    #> Performance Metric: PR #> Number of Observations: 692 #> Number of Features: 13 -#> Models Trained: 2018-04-02 15:41:20 +#> Models Trained: 2018-04-02 16:01:59 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -253,7 +253,7 @@

    Prediction

    predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

    predict(models)
    -#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:41:18
    +#>  "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:01:56
     #>  Performance in training: PR = 0.9
     #>  # A tibble: 692 x 15
     #>    diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
    @@ -285,7 +285,7 @@ 

    #> Running cross validation for Random Forest #> Running cross validation for k-Nearest Neighbors summary(regression_models) -#> Models trained: 2018-04-02 15:41:33 +#> Models trained: 2018-04-02 16:02:12 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best performance: RMSE = 9.07 @@ -331,7 +331,7 @@

    #> Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. #> weight_class: ??? #> Prepping data based on provided recipe -#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 15:41:33 +#> "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:02:12 #> Performance in training: RMSE = 9.07 #> # A tibble: 1 x 9 #> predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin diff --git a/docs/dev/index.html b/docs/dev/index.html index 8c113f9d6..609947137 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -127,19 +127,19 @@

    # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 15:39:38 +# > Models Trained: 2018-04-02 16:00:16 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best model: Random Forest -# > ROC = 0.85 +# > ROC = 0.84 # > Optimal hyperparameter values: -# > mtry = 5 +# > mtry = 6 # > splitrule = extratrees # > min.node.size = 19

    Make predictions and examine predictive performance:

    -

    +

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e281aead7..3e5f47396 100644 --- a/docs/dev/reference/flash_models.html +++ b/docs/dev/reference/flash_models.html @@ -207,7 +207,7 @@

    Examp kernel = "gaussian" ) ) - )

    Examp #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 15:39:49 +#> Models Trained: 2018-04-02 16:00:27 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -232,7 +232,7 @@

    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 15:39:51 + )
    #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
    #> diabetes looks categorical, so training classification algorithms.
    summary(models)
    #> Models trained: 2018-04-02 16:00:29 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best algorithm: Random Forest with ROC = 0.84 diff --git a/docs/dev/reference/hcai_impute.html b/docs/dev/reference/hcai_impute.html index 08eb9d248..246fdbf6c 100644 --- a/docs/dev/reference/hcai_impute.html +++ b/docs/dev/reference/hcai_impute.html @@ -179,17 +179,8 @@

    Value

    Examples

    -
    library(recipes)
    #> Loading required package: dplyr
    #> -#> Attaching package: ‘dplyr’
    #> The following object is masked from ‘package:testthat’: -#> -#> matches
    #> The following objects are masked from ‘package:stats’: -#> -#> filter, lag
    #> The following objects are masked from ‘package:base’: -#> -#> intersect, setdiff, setequal, union
    #> Loading required package: broom
    #> -#> Attaching package: ‘recipes’
    #> The following object is masked from ‘package:stats’: -#> -#> step
    +
    library(recipes) + n = 100 set.seed(9) d <- tibble::tibble(patient_id = 1:n, diff --git a/docs/dev/reference/machine_learn.html b/docs/dev/reference/machine_learn.html index f87881a5d..bcfd78226 100644 --- a/docs/dev/reference/machine_learn.html +++ b/docs/dev/reference/machine_learn.html @@ -201,7 +201,7 @@

    Examp # Clean and prep the data, tune algorithms over hyperparameter values to predict diabetes diabetes_models <- machine_learn(training_data, outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make predictions (predicted probability of diabetes) on test data -predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:39:59 +predict(diabetes_models, test_data)
    #> Prepping data based on provided recipe
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:00:37 #> Performance in training: ROC = 0.75
    #> # A tibble: 10 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> @@ -222,7 +222,7 @@

    Examp # Predict numeric outcomes simply by specifying the name of the outcome variable age_model <- machine_learn(training_data, outcome = age)

    #> Training new data prep recipe
    #> age looks numeric, so training regression algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # If new data isn't specifed, get predictions on training data. Plot predictions -predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 15:40:02 +predict(age_model)
    #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:00:41 #> Performance in training: RMSE = 9.88
    #> # A tibble: 50 x 17 #> age predicted_age patient_id pregnancies plasma_glucose diastolic_bp #> * <int> <dbl> <int> <int> <int> <dbl> @@ -251,7 +251,7 @@

    Examp #> Performance Metric: ROC #> Number of Observations: 50 #> Number of Features: 13 -#> Models Trained: 2018-04-02 15:40:04 +#> Models Trained: 2018-04-02 16:00:42 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest diff --git a/docs/dev/reference/predict.model_list.html b/docs/dev/reference/predict.model_list.html index f23a582cb..8a3f2a4c8 100644 --- a/docs/dev/reference/predict.model_list.html +++ b/docs/dev/reference/predict.model_list.html @@ -189,7 +189,7 @@

    Examp models <- machine_learn(pima_diabetes[1:50, ], outcome = diabetes)

    #> Training new data prep recipe
    #> diabetes looks categorical, so training classification algorithms.
    #> Running cross validation for Random Forest
    #> Running cross validation for k-Nearest Neighbors
    # Make prediction on the next 20 rows. This uses the best-performing model from # tuning cross validation, and it also prepares the new data in the same way as # the training data was prepared. -predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 15:40:24 +predictions <- predict(models, newdata = pima_diabetes[51:70, ])
    #> Prepping data based on provided recipe
    predictions
    #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:01:02 #> Performance in training: ROC = 0.73
    #> # A tibble: 20 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> diff --git a/man/figures/README-plot_predictions-1.png b/man/figures/README-plot_predictions-1.png new file mode 100644 index 0000000000000000000000000000000000000000..b7840b9ba3640d3fbf78f0ea5012b1df0a05e832 GIT binary patch literal 70655 zcmeGE_g7O}_XUgx1ZfxPpdjTcNC!oV(ov*JhtNThUInC=0E!?8C{;n4NGJ5(f(RlY zy?5!oL#Ro@)nL3E;i3J6_Q4GIbBzSk=r=;F|M#0iAROu%)@3M z)3Ko`bSk_`j**7Nnc1vtDTgF93TG!%reeFEx3c)zEXi#ut>pRkQ*^4sSL@(? zhi}ix}C-C?elG3*g-F3KLgB##}F@GT%NORkD9%A}%tFs(*V|Gb`!Q)cx|hv?rLmT#t>k zMhbYYRKC91$$5rv+;INgw8UtYM?|A??pkuKZug1l$@;kflKb5D%P-i1?rxP!o59bz z(#PL6?Dbqdbm$!}&(rSoeQbTx%DC^myP!)Z=O5qxak=QgSX|$U?}}VO0{*G(3G|w3 zglxgV%Y%?|o(6V54%GLCF24@HF28QSug19V#X6Jc8)x~RGrO98N80Yw8_f97dR?uu zT8+0WQ2P6(Jq;7BOSAB-##4bk_P+?9JkeICon2z64q*C<2vmi3|e+gM@kqiWs#^c!(%i<&m%T4GvMxg~=#WFci) z?r9FVXXlJVxsIipYyHor{c!aML><>jyJ$R=`(w=Li-fu26W3NkbFRMr@i@|2D`eck 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zjHJb)sO4aeGA|8G&*=R0PK;BToVu8xx3~fJ+@RPtnmgzQPR6$Ty_^U|!{zEywK$Fh z$9Ipt2df=T1RuU*FiG!HWNI=X4SkoFt(C)JblP84>=?jM!Edm07K(CH7#D7K*JggZ zEYMkOcJ%Td=208^b?v&QR$`}vX1d|H)0MyO6BaoGM>1y)m`425s`QB=_XhQZ;0V2A znA!WxtK2dF>fc4(MbzHwRVlm}@-IR*=sl~mOer4uy>PA3tuM3q*SHpI#3(H4eO%J$ z6&>+^u5QD+4VbhUMGKL>USQ!JMcMek^OTOQ*j?4+Lp+$=hD5;ZGS!S-$qg}K*Ql;3JDUM z7yh3wpt#{cp!3j$h3>!1Wc*+MJ5Y$Ap6O8h=iB@*e~@zr+?W817V>}HpnvZxcMxKk z*$Sei6n|F#-_Q2XkL1`He#`{F|9|@E-mp?%@Yjz9!faswx|#p}_WtZ(g}93l|Ce5P iK6U~9zx&l2&+(DTN*B%#HT7}8FBJu~TZQt5zW)Q8_Zyr5 literal 0 HcmV?d00001 From 4500ef7d3d0d9bb4ff106fea833b4df2766de609 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 16:49:22 -0600 Subject: [PATCH 7/9] speed up examples --- R/flash_models.R | 18 +- R/machine_learn.R | 30 +- R/model_list_generics.R | 3 +- R/plot_predictions.R | 11 +- R/predict.R | 11 +- docs/dev/articles/healthcareai.html | 284 +++++++++--------- docs/dev/index.html | 10 +- .../figure-html/unnamed-chunk-3-1.png | Bin 73364 -> 68597 bytes docs/dev/reference/flash_models.html | 4 +- docs/dev/reference/hcai_impute.html | 13 +- docs/dev/reference/machine_learn.html | 108 ++++--- docs/dev/reference/pivot.html | 28 +- .../reference/plot.hcai_predicted_df-1.png | Bin 57924 -> 58671 bytes .../reference/plot.hcai_predicted_df-2.png | Bin 85758 -> 86647 bytes docs/dev/reference/plot.model_list-1.png | Bin 97626 -> 93382 bytes docs/dev/reference/plot.model_list-2.png | Bin 64027 -> 60013 bytes docs/dev/reference/predict.model_list-1.png | Bin 74017 -> 75156 bytes docs/dev/reference/predict.model_list.html | 44 +-- docs/dev/reference/prep_data.html | 20 +- docs/dev/reference/split_train_test.html | 18 +- man/flash_models.Rd | 18 +- man/machine_learn.Rd | 30 +- man/plot.hcai_predicted_df.Rd | 11 +- man/plot.model_list.Rd | 3 +- man/predict.model_list.Rd | 11 +- vignettes/healthcareai.R | 2 +- vignettes/healthcareai.Rmd | 4 +- vignettes/healthcareai.html | 284 +++++++++--------- 28 files changed, 505 insertions(+), 460 deletions(-) diff --git a/R/flash_models.R b/R/flash_models.R index fe89f6b28..3bb821f28 100644 --- a/R/flash_models.R +++ b/R/flash_models.R @@ -34,6 +34,7 @@ #' faster.}} #' #' @examples +#' \dontrun{ #' # Prepare data #' prepped_data <- prep_data(pima_diabetes, patient_id, outcome = diabetes) #' @@ -60,15 +61,14 @@ #' summary(models) #' #' # Speed comparison of no tuning with flash_models vs. tuning with tune_models: -#' \dontrun{ -#' # ~40 seconds: -#' system.time( -#' tune_models(prepped_data, diabetes) -#' ) -#' # ~6 seconds: -#' system.time( -#' flash_models(prepped_data, diabetes) -#' ) +#' # ~40 seconds: +#' system.time( +#' tune_models(prepped_data, diabetes) +#' ) +#' # ~6 seconds: +#' system.time( +#' flash_models(prepped_data, diabetes) +#' ) #' } flash_models <- function(d, outcome, diff --git a/R/machine_learn.R b/R/machine_learn.R index 6d44ad62f..a2e6d9ce6 100644 --- a/R/machine_learn.R +++ b/R/machine_learn.R @@ -30,31 +30,37 @@ #' wraps. For finer control of model tuning use \code{\link{tune_models}}. #' #' @examples -#' # Split data into training and test sets using a subset of the data for speed -#' training_data <- pima_diabetes[1:50, ] -#' test_data <- pima_diabetes[51:60, ] +#' # Split the data into training and test sets. Use only 20 rows for speed +#' d <- split_train_test(d = pima_diabetes[1:20, ], +#' outcome = diabetes, +#' percent_train = .9) #' #' ### Classification ### #' -#' # Clean and prep the data, tune algorithms over hyperparameter values to predict diabetes -#' diabetes_models <- machine_learn(training_data, outcome = diabetes) +#' # Clean and prep the training data, specifying that patient_id is an ID column, +#' # and tune algorithms over hyperparameter values to predict diabetes +#' diabetes_models <- machine_learn(d$train, patient_id, outcome = diabetes) +#' +#' # Inspect model specification and performance +#' diabetes_models #' #' # Make predictions (predicted probability of diabetes) on test data -#' predict(diabetes_models, test_data) +#' predict(diabetes_models, d$test) #' #' ### Regression ### #' -#' # Predict numeric outcomes simply by specifying the name of the outcome variable -#' age_model <- machine_learn(training_data, outcome = age) +#' # If the outcome variable is numeric, regression models will be trained +#' age_model <- machine_learn(d$train, patient_id, outcome = age) #' -#' # If new data isn't specifed, get predictions on training data. Plot predictions +#' # If new data isn't specifed, get predictions on training data #' predict(age_model) #' #' ### Faster model training without tuning hyperparameters ### #' -#' # Train models at set hyperparameter values by setting tune to FALSE. -#' # This is faster (especially on larger datasets), but produces models with less predictive accuracy. -#' machine_learn(training_data, outcome = diabetes, tune = FALSE) +#' # Train models at set hyperparameter values by setting tune to FALSE. This is +#' # faster (especially on larger datasets), but produces models with less +#' # predictive accuracy. +#' machine_learn(d$train, patient_id, outcome = diabetes, tune = FALSE) machine_learn <- function(d, ..., outcome, models, tune = TRUE, n_folds = 5, tune_depth = 10, impute = TRUE) { diff --git a/R/model_list_generics.R b/R/model_list_generics.R index dacfe5ca6..6088e871d 100644 --- a/R/model_list_generics.R +++ b/R/model_list_generics.R @@ -98,9 +98,8 @@ summary.model_list <- function(object, ...) { #' @importFrom purrr map_df #' @export #' @examples -#' models <- tune_models(mtcars, mpg) +#' models <- tune_models(mtcars, mpg, models = "knn", tune_depth = 5) #' plot(models) -#' plot(as.model_list(models$`Random Forest`)) plot.model_list <- function(x, print = TRUE, ...) { if (!length(x)) stop("x is empty.") diff --git a/R/plot_predictions.R b/R/plot_predictions.R index 27a09d821..132aa5dbd 100644 --- a/R/plot_predictions.R +++ b/R/plot_predictions.R @@ -11,13 +11,14 @@ #' @export #' #' @details The following arguments can be provided to customize the plot: For -#' regression: title, point_size, point_alpha, font_size. For -#' classification: title, fill_colors, fill_alpha, curve_flex, font_size. For -#' details on how to use them, see \code{\link{plot_regression_predictions}} -#' or \code{\link{plot_classification_predictions}}. +#' regression: title, point_size, point_alpha, font_size. For classification: +#' title, fill_colors, fill_alpha, curve_flex, font_size. For details on how +#' to use them, see \code{\link{plot_regression_predictions}} or +#' \code{\link{plot_classification_predictions}}. #' #' @examples -#' models <- machine_learn(pima_diabetes[1:50, ], patient_id, outcome = plasma_glucose) +#' models <- machine_learn(pima_diabetes[1:50, ], patient_id, outcome = plasma_glucose, +#' models = "rf", tune = FALSE) #' predictions <- predict(models) #' plot(predictions) #' plot(predictions, title = "This model's predictions regress to the mean", diff --git a/R/predict.R b/R/predict.R index d01c1bac3..d6465e799 100644 --- a/R/predict.R +++ b/R/predict.R @@ -31,12 +31,15 @@ #' returning your predictions with the newdata in its original format. #' #' @examples -#' # Tune models using only the first 50 rows to keep computation fast -#' models <- machine_learn(pima_diabetes[1:50, ], outcome = diabetes) -#' # Make prediction on the next 20 rows. This uses the best-performing model from +#' # Tune models using only the first 20 rows to keep computation fast +#' +#' models <- machine_learn(pima_diabetes[1:20, ], patient_id, outcome = diabetes) +#' +#' # Make prediction on the next 5 rows. This uses the best-performing model from #' # tuning cross validation, and it also prepares the new data in the same way as #' # the training data was prepared. -#' predictions <- predict(models, newdata = pima_diabetes[51:70, ]) +#' +#' predictions <- predict(models, newdata = pima_diabetes[21:25, ]) #' predictions #' plot(predictions) predict.model_list <- function(object, newdata, prepdata, ...) { diff --git a/docs/dev/articles/healthcareai.html b/docs/dev/articles/healthcareai.html index c9c9e7128..6d36b1bcb 100644 --- a/docs/dev/articles/healthcareai.html +++ b/docs/dev/articles/healthcareai.html @@ -103,63 +103,63 @@

    Getting Started with healthcareai

    healthcareai comes with a built in dataset documenting diabetes among adult Pima females. Once you attach the package, the dataset is available in the variable pima_diabetes. Let’s take a look at the data with the str function. There are 768 records in 10 variables including one identifier column, several nominal variables, and substantial missingness (represented in R by NA).

    +# > Classes 'tbl_df', 'tbl' and 'data.frame': 768 obs. of 10 variables: +# > $ patient_id : int 1 2 3 4 5 6 7 8 9 10 ... +# > $ pregnancies : int 6 1 8 1 0 5 3 10 2 8 ... +# > $ plasma_glucose: int 148 85 183 89 137 116 78 115 197 125 ... +# > $ diastolic_bp : int 72 66 64 66 40 74 50 NA 70 96 ... +# > $ skinfold : int 35 29 NA 23 35 NA 32 NA 45 NA ... +# > $ insulin : int NA NA NA 94 168 NA 88 NA 543 NA ... +# > $ weight_class : chr "obese" "overweight" "normal" "overweight" ... +# > $ pedigree : num 0.627 0.351 0.672 0.167 2.288 ... +# > $ age : int 50 31 32 21 33 30 26 29 53 54 ... +# > $ diabetes : chr "Y" "N" "Y" "N" ...

    Easy Machine Learning

    If you don’t want to fuss with details any more than necessary, machine_learn is the function for you. It makes it as easy as possible to implement machine learning models by putting all the detais in the background so that you don’t have to worry about them. Of course it might be wise to worry about them, and we’ll get to how to do that further down, but for now, you can automatically take care of problems in the data, do basic feature engineering, and tune multiple machine learning models using cross validation with machine_learn.

    machine_learn always gets the name of the data frame, then any columns that should not be used by the model (uninformative columns, such as IDs), then the variable to be predicted with outcome =. If you want machine_learn to run faster, you can have that—at the expense of a bit of predictive power—by setting its tune argument to FALSE.

    +# > Training new data prep recipe +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > Running cross validation for Random Forest +# > Running cross validation for k-Nearest Neighbors

    machine_learn has told us that it has created a recipe for data preparation (this allows us to do exactly the same data cleaning and feature engineering when you want predictions on a new dataset), is ignoring patient_id when tuning models as we told it to, is training classification algorithms because the outcome variable diabetes is categorical, and has executed cross validation for two machine learning models: random forests, and k-nearest neighbors. Let’s see what the models look like.

    +# > Algorithms Trained: Random Forest, k-Nearest Neighbors +# > Target: diabetes +# > Class: Classification +# > Performance Metric: ROC +# > Number of Observations: 768 +# > Number of Features: 12 +# > Models Trained: 2018-04-02 16:27:57 +# > +# > Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. +# > Best model: Random Forest +# > ROC = 0.84 +# > Optimal hyperparameter values: +# > mtry = 5 +# > splitrule = extratrees +# > min.node.size = 12

    Everything looks as expected, and the best model is is a random forest that achives performance of AUROC = 0.84. Not bad for one line of code.

    Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

    +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:27:57 +# > Performance in training: ROC = 0.84 +# > # A tibble: 768 x 14 +# > diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp +# > * <fct> <dbl> <int> <dbl> <dbl> +# > 1 Y 0.796 6 148. 72. +# > 2 N 0.0740 1 85. 66. +# > 3 Y 0.608 8 183. 64. +# > 4 N 0.00639 1 89. 66. +# > 5 Y 0.717 0 137. 40. +# > # ... with 763 more rows, and 9 more variables: skinfold <dbl>, +# > # insulin <dbl>, pedigree <dbl>, age <int>, weight_class_normal <dbl>, +# > # weight_class_obese <dbl>, weight_class_overweight <dbl>, +# > # weight_class_other <dbl>, weight_class_hcai_missing <dbl>

    We get a message about when the model was trained and how well it preformed in training, and we get back a data frame that looks sort of like the original, but has a new column predited_diabetes that contains the model-generated probability each individual has diabetes, and contains changes that were made preparing the data for model training, e.g. missingness has been filled in and weight_class has been split into a series of “dummy” variables.

    We can plot how effectively the model is able to separate diabetic from non-diabetic individuals by calling the plot function on the output of predict.

    @@ -170,17 +170,17 @@

    Data Profiling

    It is always a good idea to be aware of where there are missing values in data. The missingness function helps with that. In addition to looking for values R sees as missing, it looks for other values that might represent missing, such as "NULL", and issues a warning if it finds any.

    +# > variable percent_missing +# > 1 patient_id 0.0 +# > 2 pregnancies 0.0 +# > 3 pedigree 0.0 +# > 4 age 0.0 +# > 5 diabetes 0.0 +# > 6 plasma_glucose 0.7 +# > 7 weight_class 1.4 +# > 8 diastolic_bp 4.6 +# > 9 skinfold 29.6 +# > 10 insulin 48.7

    It’s good that we don’t have any missingness in our ID or outcome columns. We’ll see how missingness in predictors is addressed further down.

    The “recipe” that the above message refers to is a set of instructions for how to transform a dataset the way we just transformed our training data. Any machine learning that we do (within healthcareai) on prepped_training_data will retain that recipe and apply it before making predictions on new data. That means that when you have models making predictions in production, you don’t have to figure out how to transform the data or worry about encountering missing data or new category levels.

    +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > You've chosen to tune 125 models (n_folds = 5 x tune_depth = 25 x length(models) = 1) on a 692 row dataset. This may take a while... +# > Running cross validation for Random Forest

    We get a message saying the training may take a while because we’re training so many models, but in this case it takes just about 20 seconds to train all those models.

    We can examine how the model performs across hyperparameters by plotting the model object. It looks like extratrees is a superior split rule for this model, and larger values of minimum node size tend to do better.

    @@ -228,23 +228,23 @@

    outcome = diabetes, models = "RF", metric = "PR") -#> Variable(s) ignored in prep_data won't be used to tune models: patient_id -#> diabetes looks categorical, so training classification algorithms. -#> Algorithms Trained: Random Forest -#> Target: diabetes -#> Class: Classification -#> Performance Metric: PR -#> Number of Observations: 692 -#> Number of Features: 13 -#> Models Trained: 2018-04-02 11:00:37 -#> -#> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. -#> Best model: Random Forest -#> PR = 0.89 -#> User-selected hyperparameter values: -#> mtry = 5 -#> splitrule = extratrees -#> min.node.size = 10 +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > Algorithms Trained: Random Forest +# > Target: diabetes +# > Class: Classification +# > Performance Metric: PR +# > Number of Observations: 692 +# > Number of Features: 13 +# > Models Trained: 2018-04-02 16:28:24 +# > +# > Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. +# > Best model: Random Forest +# > PR = 0.89 +# > User-selected hyperparameter values: +# > mtry = 5 +# > splitrule = extratrees +# > min.node.size = 10

    In this case we sacrificed just 0.01 AUPR versus tuning the models. In our experience, that’s on the small side of typical. A good workflow is often to do all of your development using flash_models, and as a final step before putting a model into production, retrain the model using tune_models.

    @@ -253,24 +253,24 @@

    Prediction

    predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

    +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:28:21 +# > Performance in training: PR = 0.9 +# > # A tibble: 692 x 15 +# > diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp +# > * <fct> <dbl> <dbl> <dbl> <dbl> +# > 1 N 0.0677 -0.843 -1.19 -0.521 +# > 2 Y 0.642 1.22 2.01 -0.686 +# > 3 N 0.00366 -0.843 -1.05 -0.521 +# > 4 Y 0.723 -1.14 0.509 -2.66 +# > 5 N 0.228 0.338 -0.175 0.138 +# > # ... with 687 more rows, and 10 more variables: skinfold <dbl>, +# > # insulin <dbl>, pedigree <dbl>, age <dbl>, weight_class_normal <dbl>, +# > # weight_class_obese <dbl>, weight_class_overweight <dbl>, +# > # weight_class_underweight <dbl>, weight_class_hcai_missing <dbl>, +# > # weight_class_other <dbl>

    To get predictions on a new dataset, pass the new data to predict, and it will automatically be prepared based on the recipe generated on the training data. We can plot the predictions to see how well our model is doing, and we see that it’s separating diabetic from non-diabetic individuals pretty well, although there a fair number of non-diabetics with high predicted probabilities of diabetes. This may be due to optimizing for precision recall, or may indicate pre-diabetic patients.

    @@ -279,44 +279,44 @@

    A Regression Example

    All the examples above have been classification tasks, predicting a yes/no outcome. Here’s an example of a full regression modeling pipeline on a silly problem: predicting individuals’ ages. The code is very similar to classification.

    regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age)
    -#>  Training new data prep recipe
    -#>  Variable(s) ignored in prep_data won't be used to tune models: patient_id
    -#>  age looks numeric, so training regression algorithms.
    -#>  Running cross validation for Random Forest
    -#>  Running cross validation for k-Nearest Neighbors
    +# > Training new data prep recipe
    +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id
    +# > age looks numeric, so training regression algorithms.
    +# > Running cross validation for Random Forest
    +# > Running cross validation for k-Nearest Neighbors
     summary(regression_models)
    -#>  Models trained: 2018-04-02 11:00:50
    -#>  
    -#>  Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values.
    -#>  Best performance: RMSE = 9.07
    -#>  By Random Forest with hyperparameters:
    -#>    mtry = 10
    -#>    splitrule = extratrees
    -#>    min.node.size = 10
    -#>  
    -#>  Out-of-fold performance of all trained models:
    -#>  
    -#>  $`Random Forest`
    -#>  # A tibble: 10 x 9
    -#>    min.node.size  mtry splitrule   RMSE Rsquared   MAE RMSESD RsquaredSD
    -#>  *         <int> <int> <fct>      <dbl>    <dbl> <dbl>  <dbl>      <dbl>
    -#>  1            10    10 extratrees  9.07    0.404  6.43  0.640     0.0358
    -#>  2             8    11 extratrees  9.09    0.402  6.43  0.626     0.0396
    -#>  3            12     5 extratrees  9.13    0.405  6.56  0.666     0.0272
    -#>  4            10    13 variance    9.33    0.376  6.60  0.633     0.0358
    -#>  5             7    10 variance    9.34    0.374  6.61  0.583     0.0303
    -#>  # ... with 5 more rows, and 1 more variable: MAESD <dbl>
    -#>  
    -#>  $`k-Nearest Neighbors`
    -#>  # A tibble: 10 x 9
    -#>     kmax distance kernel       RMSE Rsquared   MAE RMSESD RsquaredSD MAESD
    -#>  * <dbl>    <dbl> <fct>       <dbl>    <dbl> <dbl>  <dbl>      <dbl> <dbl>
    -#>  1   16.    2.60  inv          9.44    0.363  6.65  0.811     0.0649 0.551
    -#>  2   14.    1.73  gaussian     9.44    0.361  6.66  0.717     0.0593 0.452
    -#>  3   13.    1.58  triangular   9.49    0.355  6.66  0.764     0.0697 0.461
    -#>  4   10.    0.933 rectangular  9.55    0.346  6.79  0.637     0.0438 0.412
    -#>  5    6.    1.68  inv          9.64    0.340  6.74  0.723     0.0677 0.465
    -#>  # ... with 5 more rows
    +# > Models trained: 2018-04-02 16:28:37 +# > +# > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. +# > Best performance: RMSE = 9.07 +# > By Random Forest with hyperparameters: +# > mtry = 10 +# > splitrule = extratrees +# > min.node.size = 10 +# > +# > Out-of-fold performance of all trained models: +# > +# > $`Random Forest` +# > # A tibble: 10 x 9 +# > min.node.size mtry splitrule RMSE Rsquared MAE RMSESD RsquaredSD +# > * <int> <int> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> +# > 1 10 10 extratrees 9.07 0.404 6.43 0.640 0.0358 +# > 2 8 11 extratrees 9.09 0.402 6.43 0.626 0.0396 +# > 3 12 5 extratrees 9.13 0.405 6.56 0.666 0.0272 +# > 4 10 13 variance 9.33 0.376 6.60 0.633 0.0358 +# > 5 7 10 variance 9.34 0.374 6.61 0.583 0.0303 +# > # ... with 5 more rows, and 1 more variable: MAESD <dbl> +# > +# > $`k-Nearest Neighbors` +# > # A tibble: 10 x 9 +# > kmax distance kernel RMSE Rsquared MAE RMSESD RsquaredSD MAESD +# > * <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +# > 1 16. 2.60 inv 9.44 0.363 6.65 0.811 0.0649 0.551 +# > 2 14. 1.73 gaussian 9.44 0.361 6.66 0.717 0.0593 0.452 +# > 3 13. 1.58 triangular 9.49 0.355 6.66 0.764 0.0697 0.461 +# > 4 10. 0.933 rectangular 9.55 0.346 6.79 0.637 0.0438 0.412 +# > 5 6. 1.68 inv 9.64 0.340 6.74 0.723 0.0677 0.465 +# > # ... with 5 more rows

    Let’s make a prediction on a hypothetical new patient. Note that the model handles missingness in insulin and a new category level in weight_class without a problem (but warns about it).

    +# > Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. +# > weight_class: ??? +# > Prepping data based on provided recipe +# > "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:28:37 +# > Performance in training: RMSE = 9.07 +# > # A tibble: 1 x 9 +# > predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin +# > * <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> +# > 1 23.9 0. 80. 55. 24. NA +# > # ... with 3 more variables: weight_class <fct>, pedigree <dbl>, +# > # diabetes <fct> diff --git a/docs/dev/index.html b/docs/dev/index.html index 9d777463b..8646c9316 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -93,7 +93,7 @@ - +

    Make predictions and examine predictive performance:

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zD0c)H6pYjA$Ec0w(-8H+sl?nJM3#t8^GI}r@S%8FO;W6kDmH&QT99+Bbbn)I<2e<*Q%R)K-o2Po3`FPWOQ zXlF~Kugy}Wmkx`&N9kNoT;-E2M4rDC+9Afs_H}9HV$+_$X)-W4H|Iy&WYjjrDD>hW z9zovpIEcXKI6*WSw_W+ht&QGxQcj^|94s@mFkUNd(v+!dsPbTE4}hLhVxD>%&n@*N zB`OA%O`-`x8s2H{y1o1IQNmAiaMI_Ga}WHxeh$0|9!VJ0ToMUBQA;|!ZT;YSmfiKs z0WM-`!)g^3!Y}`H1fV()8Zzy_m}LK|Jj61?c&OfW=0@^r8wfItMQgJOs=tV)^>t|v zFLw2}4XzX9dewoHX3n^iYVcgc-6*oYsjW~znilvr&EnJqWz&w(Ddrumj&G{DswC1U zH@D#Z(h#8T>bakdJ`2~NA2aM%tXnC$x(XPGD{&bL{Zl;aT|p(92qaH^gl)I0(^g2U z(_95$DUqn?f69MVe6$dMFWmaeGrXRz9_0}p7ye_dEAp3cU%><$Ny?LqdM}sT#x3$- z8(pF6#U&p=;L~c2tdh#!Qq{PuMkQot({lHd=}nDqD@gq`b;nue(RoSoL>k(r8DQY6gp;F}~ z*TmoI2G9l)O9~^7lQMdf%JiXlJwYQV80yyjgQ&*g-Z|_LX@6nj_-CsDKTm8Avo@PR z_{M(Za*J>~==$w``PXkBNJZ`L#SKznar|8YVvY@i(|PZyt7Cn$uu2%m<5Lj>eAzp; zEaoQ8-&p@$C`2d$UU<_h<9{DoldZ4VRNmCIyExamp #> Performance Metric: ROC #> Number of Observations: 768 #> Number of Features: 12 -#> Models Trained: 2018-04-02 10:59:04 +#> Models Trained: 2018-04-02 16:26:49 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest @@ -232,7 +232,7 @@

      Examp kernel = "gaussian" ) ) - )
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> diabetes looks categorical, so training classification algorithms.
      summary(models)
      #> Models trained: 2018-04-02 10:59:06 + )
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> diabetes looks categorical, so training classification algorithms.
      summary(models)
      #> Models trained: 2018-04-02 16:26:51 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best algorithm: Random Forest with ROC = 0.84 diff --git a/docs/dev/reference/hcai_impute.html b/docs/dev/reference/hcai_impute.html index 246fdbf6c..08eb9d248 100644 --- a/docs/dev/reference/hcai_impute.html +++ b/docs/dev/reference/hcai_impute.html @@ -179,8 +179,17 @@

      Value

      Examples

      -
      library(recipes) - +
      library(recipes)
      #> Loading required package: dplyr
      #> +#> Attaching package: ‘dplyr’
      #> The following object is masked from ‘package:testthat’: +#> +#> matches
      #> The following objects are masked from ‘package:stats’: +#> +#> filter, lag
      #> The following objects are masked from ‘package:base’: +#> +#> intersect, setdiff, setequal, union
      #> Loading required package: broom
      #> +#> Attaching package: ‘recipes’
      #> The following object is masked from ‘package:stats’: +#> +#> step
      n = 100 set.seed(9) d <- tibble::tibble(patient_id = 1:n, diff --git a/docs/dev/reference/machine_learn.html b/docs/dev/reference/machine_learn.html index 7c63e081d..95c92213d 100644 --- a/docs/dev/reference/machine_learn.html +++ b/docs/dev/reference/machine_learn.html @@ -192,70 +192,88 @@

      Details

      Examples

      -
      # Split data into training and test sets using a subset of the data for speed -training_data <- pima_diabetes[1:50, ] -test_data <- pima_diabetes[51:60, ] +
      # Split first 100 rows of dataset into training and test sets +d <- split_train_test(d = pima_diabetes[1:100, ], + outcome = diabetes, + percent_train = .9) ### Classification ### -# Clean and prep the data, tune algorithms over hyperparameter values to predict diabetes -diabetes_models <- machine_learn(training_data, outcome = diabetes)
      #> Training new data prep recipe
      #> diabetes looks categorical, so training classification algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      +# Clean and prep the training data, specifying that patient_id is an ID column, +# and tune algorithms over hyperparameter values to predict diabetes +diabetes_models <- machine_learn(d$train, patient_id, outcome = diabetes)
      #> Training new data prep recipe
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> diabetes looks categorical, so training classification algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      +# Inspect model specification and performance +diabetes_models
      #> Algorithms Trained: Random Forest, k-Nearest Neighbors +#> Target: diabetes +#> Class: Classification +#> Performance Metric: ROC +#> Number of Observations: 91 +#> Number of Features: 12 +#> Models Trained: 2018-04-02 16:27:00 +#> +#> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. +#> Best model: Random Forest +#> ROC = 0.77 +#> Optimal hyperparameter values: +#> mtry = 4 +#> splitrule = gini +#> min.node.size = 9
      # Make predictions (predicted probability of diabetes) on test data -predict(diabetes_models, test_data)
      #> Prepping data based on provided recipe
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 10:59:13 -#> Performance in training: ROC = 0.75
      #> # A tibble: 10 x 11 -#> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp -#> * <chr> <dbl> <int> <int> <int> <int> -#> 1 N 0.255 51 1 103 80 -#> 2 N 0.278 52 1 101 50 -#> 3 N 0.260 53 5 88 66 -#> 4 Y 0.663 54 8 176 90 -#> 5 N 0.716 55 7 150 66 -#> 6 N 0.267 56 1 73 50 -#> 7 Y 0.706 57 7 187 68 -#> 8 N 0.558 58 0 100 88 -#> 9 N 0.503 59 0 146 82 -#> 10 N 0.432 60 0 105 64 +predict(diabetes_models, d$test)
      #> Prepping data based on provided recipe
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:27:00 +#> Performance in training: ROC = 0.77
      #> # A tibble: 9 x 11 +#> diabetes predicted_diabet… patient_id pregnancies plasma_glucose diastolic_bp +#> * <chr> <dbl> <int> <int> <int> <int> +#> 1 Y 0.196 7 3 78 50 +#> 2 Y 0.567 17 0 118 84 +#> 3 N 0.581 31 5 109 75 +#> 4 N 0.478 36 4 103 60 +#> 5 N 0.335 47 1 146 56 +#> 6 N 0.189 70 4 146 85 +#> 7 Y 0.379 73 13 126 90 +#> 8 N 0.377 78 5 95 72 +#> 9 N 0.176 83 7 83 78 #> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, #> # pedigree <dbl>, age <int>
      ### Regression ### -# Predict numeric outcomes simply by specifying the name of the outcome variable -age_model <- machine_learn(training_data, outcome = age)
      #> Training new data prep recipe
      #> age looks numeric, so training regression algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      -# If new data isn't specifed, get predictions on training data. Plot predictions -predict(age_model)
      #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 10:59:16 -#> Performance in training: RMSE = 9.88
      #> # A tibble: 50 x 17 -#> age predicted_age patient_id pregnancies plasma_glucose diastolic_bp -#> * <int> <dbl> <int> <int> <int> <dbl> -#> 1 50 44.1 1 6 148 72.0 -#> 2 31 29.0 2 1 85 66.0 -#> 3 32 36.4 3 8 183 64.0 -#> 4 21 25.1 4 1 89 66.0 -#> 5 33 33.3 5 0 137 40.0 -#> 6 30 32.5 6 5 116 74.0 -#> 7 26 27.8 7 3 78 50.0 -#> 8 29 33.6 8 10 115 73.6 -#> 9 53 47.8 9 2 197 70.0 -#> 10 54 46.9 10 8 125 96.0 -#> # ... with 40 more rows, and 11 more variables: skinfold <dbl>, insulin <dbl>, -#> # pedigree <dbl>, weight_class_normal <dbl>, weight_class_obese <dbl>, +# If the outcome variable is numeric, regression models will be trained +age_model <- machine_learn(d$train, patient_id, outcome = age)
      #> Training new data prep recipe
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> age looks numeric, so training regression algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      +# If new data isn't specifed, get predictions on training data +predict(age_model)
      #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:27:04 +#> Performance in training: RMSE = 8.84
      #> # A tibble: 91 x 16 +#> age predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin +#> * <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> +#> 1 50 43.0 6 148. 72.0 35.0 169. +#> 2 31 25.4 1 85. 66.0 29.0 169. +#> 3 32 41.5 8 183. 64.0 29.0 169. +#> 4 21 24.3 1 89. 66.0 23.0 94.0 +#> 5 33 33.0 0 137. 40.0 35.0 168. +#> 6 30 31.4 5 116. 74.0 29.0 169. +#> 7 29 37.6 10 115. 72.4 29.0 169. +#> 8 53 47.7 2 197. 70.0 45.0 543. +#> 9 54 45.6 8 125. 96.0 29.0 169. +#> 10 30 33.2 4 110. 92.0 29.0 169. +#> # ... with 81 more rows, and 9 more variables: pedigree <dbl>, +#> # weight_class_normal <dbl>, weight_class_obese <dbl>, #> # weight_class_overweight <dbl>, weight_class_hcai_missing <dbl>, #> # weight_class_other <dbl>, diabetes_Y <dbl>, diabetes_other <dbl>, #> # diabetes_hcai_missing <dbl>
      ### Faster model training without tuning hyperparameters ### -# Train models at set hyperparameter values by setting tune to FALSE. -# This is faster (especially on larger datasets), but produces models with less predictive accuracy. -machine_learn(training_data, outcome = diabetes, tune = FALSE)
      #> Training new data prep recipe
      #> diabetes looks categorical, so training classification algorithms.
      #> Algorithms Trained: Random Forest, k-Nearest Neighbors +# Train models at set hyperparameter values by setting tune to FALSE. This is +# faster (especially on larger datasets), but produces models with less +# predictive accuracy. +machine_learn(d$train, patient_id, outcome = diabetes, tune = FALSE)
      #> Training new data prep recipe
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> diabetes looks categorical, so training classification algorithms.
      #> Algorithms Trained: Random Forest, k-Nearest Neighbors #> Target: diabetes #> Class: Classification #> Performance Metric: ROC -#> Number of Observations: 50 -#> Number of Features: 13 -#> Models Trained: 2018-04-02 10:59:18 +#> Number of Observations: 91 +#> Number of Features: 12 +#> Models Trained: 2018-04-02 16:27:05 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest -#> ROC = 0.67 +#> ROC = 0.77 #> User-selected hyperparameter values: #> mtry = 5 #> splitrule = extratrees diff --git a/docs/dev/reference/pivot.html b/docs/dev/reference/pivot.html index 7daa28b15..7908fa2b1 100644 --- a/docs/dev/reference/pivot.html +++ b/docs/dev/reference/pivot.html @@ -226,26 +226,26 @@

      Examp bills

      #> # A tibble: 8 x 4 #> patient_id dept_id charge date #> <chr> <chr> <dbl> <date> -#> 1 A ED 9051. 2024-12-24 -#> 2 A ICU 4995. 2024-12-25 -#> 3 A ED 2687. 2024-12-25 -#> 4 A ICU 5475. 2024-12-23 -#> 5 B ED 8745. 2024-12-24 -#> 6 B ICU 9077. 2024-12-23 -#> 7 B ED 3351. 2024-12-23 -#> 8 B ICU 7358. 2024-12-25
      +#> 1 A ED 2410. 2024-12-23 +#> 2 A ICU 9265. 2024-12-24 +#> 3 A ED 118. 2024-12-24 +#> 4 A ICU 1650. 2024-12-24 +#> 5 B ED 3184. 2024-12-25 +#> 6 B ICU 4829. 2024-12-24 +#> 7 B ED 196. 2024-12-23 +#> 8 B ICU 2433. 2024-12-25
      # Total charges per patient x department: pivot(bills, patient_id, dept_id, charge, sum)
      #> # A tibble: 2 x 3 #> patient_id dept_id_ED dept_id_ICU #> <fct> <dbl> <dbl> -#> 1 A 11738. 10471. -#> 2 B 12096. 16435.
      +#> 1 A 2528. 10915. +#> 2 B 3381. 7262.
      # Count of charges per patient x day: pivot(bills, patient_id, date)
      #> No fill column was provided, so using "1" for present entities
      #> There are rows that contain the same values of both patient_id and date but you didn't provide a function to 'fun' for their aggregation. Proceeding with the default: fun = sum.
      #> # A tibble: 2 x 4 #> patient_id `date_2024-12-23` `date_2024-12-24` `date_2024-12-25` #> <fct> <int> <int> <int> -#> 1 A 1 1 2 -#> 2 B 2 1 1
      +#> 1 A 1 3 NA +#> 2 B 1 1 2
      # Can provide a custom function to fun, which will take fill as input. # Get the difference between the greatest and smallest charge in each # department for each patient and format it as currency. @@ -257,8 +257,8 @@

      Examp )

      #> # A tibble: 2 x 3 #> patient_id dept_id_ED dept_id_ICU #> <fct> <chr> <chr> -#> 1 A $6364.57 $479.92 -#> 2 B $5393.72 $1718.13
      +#> 1 A $2292.21 $7614.78 +#> 2 B $2987.89 $2395.96
      #> Training new data prep recipe
      #> diabetes looks categorical, so training classification algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      # Make prediction on the next 20 rows. This uses the best-performing model from # tuning cross validation, and it also prepares the new data in the same way as # the training data was prepared. -predictions <- predict(models, newdata = pima_diabetes[51:70, ])
      #> Prepping data based on provided recipe
      predictions
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 10:59:38 -#> Performance in training: ROC = 0.73
      #> # A tibble: 20 x 11 +predictions <- predict(models, newdata = pima_diabetes[51:70, ])
      #> Prepping data based on provided recipe
      predictions
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:27:23 +#> Performance in training: ROC = 0.7
      #> # A tibble: 20 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> -#> 1 N 0.131 51 1 103 80 -#> 2 N 0.160 52 1 101 50 -#> 3 N 0.114 53 5 88 66 -#> 4 Y 0.743 54 8 176 90 -#> 5 N 0.816 55 7 150 66 -#> 6 N 0.108 56 1 73 50 -#> 7 Y 0.814 57 7 187 68 -#> 8 N 0.638 58 0 100 88 -#> 9 N 0.360 59 0 146 82 -#> 10 N 0.540 60 0 105 64 -#> 11 N 0.114 61 2 84 NA -#> 12 Y 0.334 62 8 133 72 -#> 13 N 0.170 63 5 44 62 -#> 14 N 0.439 64 2 141 58 -#> 15 Y 0.366 65 7 114 66 -#> 16 N 0.139 66 5 99 74 -#> 17 Y 0.316 67 0 109 88 -#> 18 N 0.274 68 2 109 92 -#> 19 N 0.113 69 1 95 66 -#> 20 N 0.137 70 4 146 85 +#> 1 N 0.120 51 1 103 80 +#> 2 N 0.149 52 1 101 50 +#> 3 N 0.114 53 5 88 66 +#> 4 Y 0.742 54 8 176 90 +#> 5 N 0.832 55 7 150 66 +#> 6 N 0.132 56 1 73 50 +#> 7 Y 0.810 57 7 187 68 +#> 8 N 0.623 58 0 100 88 +#> 9 N 0.414 59 0 146 82 +#> 10 N 0.459 60 0 105 64 +#> 11 N 0.134 61 2 84 NA +#> 12 Y 0.441 62 8 133 72 +#> 13 N 0.210 63 5 44 62 +#> 14 N 0.385 64 2 141 58 +#> 15 Y 0.498 65 7 114 66 +#> 16 N 0.191 66 5 99 74 +#> 17 Y 0.382 67 0 109 88 +#> 18 N 0.276 68 2 109 92 +#> 19 N 0.0970 69 1 95 66 +#> 20 N 0.177 70 4 146 85 #> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, #> # pedigree <dbl>, age <int>
      plot(predictions)
      diff --git a/docs/dev/reference/prep_data.html b/docs/dev/reference/prep_data.html index de683f24e..f3d32f22f 100644 --- a/docs/dev/reference/prep_data.html +++ b/docs/dev/reference/prep_data.html @@ -354,16 +354,16 @@

      Examp #> Adding levels to: other, hcai_missing [trained]

      #> Current data:
      #> # A tibble: 700 x 10 #> patient_id pregnancies plasma_glucose diastolic_bp skinfold insulin #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.646 0.871 -0.0211 0.635 0.344 -#> 2 2 -0.840 -1.19 -0.514 0.0152 -0.855 -#> 3 3 1.24 2.02 -0.678 -0.956 0.546 -#> 4 4 -0.840 -1.06 -0.514 -0.605 -0.613 -#> 5 5 -1.14 0.511 -2.65 0.635 0.132 -#> 6 6 0.349 -0.175 0.143 -0.525 -0.252 -#> 7 7 -0.246 -1.42 -1.83 0.325 -0.673 -#> 8 8 1.83 -0.208 -0.0957 0.507 -0.0956 -#> 9 9 -0.543 2.47 -0.185 1.67 3.90 -#> 10 10 1.24 0.119 1.95 0.477 0.837 +#> 1 1 0.646 0.872 -0.0199 0.637 0.530 +#> 2 2 -0.840 -1.19 -0.513 0.0129 -0.843 +#> 3 3 1.24 2.02 -0.677 -0.901 0.343 +#> 4 4 -0.840 -1.06 -0.513 -0.611 -0.608 +#> 5 5 -1.14 0.512 -2.65 0.637 0.132 +#> 6 6 0.349 -0.175 0.144 -0.318 -0.223 +#> 7 7 -0.246 -1.42 -1.83 0.325 -0.668 +#> 8 8 1.83 -0.208 -0.0654 0.353 -0.102 +#> 9 9 -0.543 2.47 -0.184 1.68 3.88 +#> 10 10 1.24 0.119 1.95 0.372 0.434 #> # ... with 690 more rows, and 4 more variables: weight_class <fct>, #> # pedigree <dbl>, age <dbl>, diabetes <fct>
      diff --git a/docs/dev/reference/split_train_test.html b/docs/dev/reference/split_train_test.html index 63b552d0e..38d57edc4 100644 --- a/docs/dev/reference/split_train_test.html +++ b/docs/dev/reference/split_train_test.html @@ -129,7 +129,7 @@

      Split data into training and test data frames

      Split data into training and test data frames

      -
      split_train_test(d, outcome, p = 0.8, seed)
      +
      split_train_test(d, outcome, percent_train = 0.8, seed)

      Arguments

      @@ -144,7 +144,7 @@

      Arg variable

      - + @@ -166,12 +166,12 @@

      Details

      Examples

      split_train_test(mtcars, am, .9)
      #> $train #> mpg cyl disp hp drat wt qsec vs am gear carb +#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 -#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 @@ -179,7 +179,6 @@

      Examp #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 -#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 @@ -188,8 +187,9 @@

      Examp #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 -#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 +#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 +#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 @@ -197,10 +197,10 @@

      Examp #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> #> $test -#> mpg cyl disp hp drat wt qsec vs am gear carb -#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 -#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 -#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 +#> mpg cyl disp hp drat wt qsec vs am gear carb +#> Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 +#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 +#> Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 #>

      Easy Machine Learning

      If you don’t want to fuss with details any more than necessary, machine_learn is the function for you. It makes it as easy as possible to implement machine learning models by putting all the detais in the background so that you don’t have to worry about them. Of course it might be wise to worry about them, and we’ll get to how to do that further down, but for now, you can automatically take care of problems in the data, do basic feature engineering, and tune multiple machine learning models using cross validation with machine_learn.

      machine_learn always gets the name of the data frame, then any columns that should not be used by the model (uninformative columns, such as IDs), then the variable to be predicted with outcome =. If you want machine_learn to run faster, you can have that—at the expense of a bit of predictive power—by setting its tune argument to FALSE.

      +# > Training new data prep recipe +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > Running cross validation for Random Forest +# > Running cross validation for k-Nearest Neighbors

      machine_learn has told us that it has created a recipe for data preparation (this allows us to do exactly the same data cleaning and feature engineering when you want predictions on a new dataset), is ignoring patient_id when tuning models as we told it to, is training classification algorithms because the outcome variable diabetes is categorical, and has executed cross validation for two machine learning models: random forests, and k-nearest neighbors. Let’s see what the models look like.

      +# > Algorithms Trained: Random Forest, k-Nearest Neighbors +# > Target: diabetes +# > Class: Classification +# > Performance Metric: ROC +# > Number of Observations: 768 +# > Number of Features: 12 +# > Models Trained: 2018-04-02 16:18:34 +# > +# > Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. +# > Best model: Random Forest +# > ROC = 0.84 +# > Optimal hyperparameter values: +# > mtry = 5 +# > splitrule = extratrees +# > min.node.size = 12

      Everything looks as expected, and the best model is is a random forest that achives performance of AUROC = 0.84. Not bad for one line of code.

      Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

      +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:18:34 +# > Performance in training: ROC = 0.84 +# > # A tibble: 768 x 14 +# > diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp +# > * <fct> <dbl> <int> <dbl> <dbl> +# > 1 Y 0.796 6 148. 72. +# > 2 N 0.0740 1 85. 66. +# > 3 Y 0.608 8 183. 64. +# > 4 N 0.00639 1 89. 66. +# > 5 Y 0.717 0 137. 40. +# > # ... with 763 more rows, and 9 more variables: skinfold <dbl>, +# > # insulin <dbl>, pedigree <dbl>, age <int>, weight_class_normal <dbl>, +# > # weight_class_obese <dbl>, weight_class_overweight <dbl>, +# > # weight_class_other <dbl>, weight_class_hcai_missing <dbl>

      We get a message about when the model was trained and how well it preformed in training, and we get back a data frame that looks sort of like the original, but has a new column predited_diabetes that contains the model-generated probability each individual has diabetes, and contains changes that were made preparing the data for model training, e.g. missingness has been filled in and weight_class has been split into a series of “dummy” variables.

      We can plot how effectively the model is able to separate diabetic from non-diabetic individuals by calling the plot function on the output of predict.

      @@ -349,17 +349,17 @@

      Easy Machine Learning

      Data Profiling

      It is always a good idea to be aware of where there are missing values in data. The missingness function helps with that. In addition to looking for values R sees as missing, it looks for other values that might represent missing, such as "NULL", and issues a warning if it finds any.

      +# > variable percent_missing +# > 1 patient_id 0.0 +# > 2 pregnancies 0.0 +# > 3 pedigree 0.0 +# > 4 age 0.0 +# > 5 diabetes 0.0 +# > 6 plasma_glucose 0.7 +# > 7 weight_class 1.4 +# > 8 diastolic_bp 4.6 +# > 9 skinfold 29.6 +# > 10 insulin 48.7

      It’s good that we don’t have any missingness in our ID or outcome columns. We’ll see how missingness in predictors is addressed further down.

      The “recipe” that the above message refers to is a set of instructions for how to transform a dataset the way we just transformed our training data. Any machine learning that we do (within healthcareai) on prepped_training_data will retain that recipe and apply it before making predictions on new data. That means that when you have models making predictions in production, you don’t have to figure out how to transform the data or worry about encountering missing data or new category levels.

      +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > You've chosen to tune 125 models (n_folds = 5 x tune_depth = 25 x length(models) = 1) on a 692 row dataset. This may take a while... +# > Running cross validation for Random Forest

      We get a message saying the training may take a while because we’re training so many models, but in this case it takes just about 20 seconds to train all those models.

      We can examine how the model performs across hyperparameters by plotting the model object. It looks like extratrees is a superior split rule for this model, and larger values of minimum node size tend to do better.

      @@ -404,23 +404,23 @@

      Faster Model Training

      outcome = diabetes, models = "RF", metric = "PR") -#> Variable(s) ignored in prep_data won't be used to tune models: patient_id -#> diabetes looks categorical, so training classification algorithms. -#> Algorithms Trained: Random Forest -#> Target: diabetes -#> Class: Classification -#> Performance Metric: PR -#> Number of Observations: 692 -#> Number of Features: 13 -#> Models Trained: 2018-04-02 05:58:33 -#> -#> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. -#> Best model: Random Forest -#> PR = 0.89 -#> User-selected hyperparameter values: -#> mtry = 5 -#> splitrule = extratrees -#> min.node.size = 10 +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id +# > diabetes looks categorical, so training classification algorithms. +# > Algorithms Trained: Random Forest +# > Target: diabetes +# > Class: Classification +# > Performance Metric: PR +# > Number of Observations: 692 +# > Number of Features: 13 +# > Models Trained: 2018-04-02 16:19:01 +# > +# > Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. +# > Best model: Random Forest +# > PR = 0.89 +# > User-selected hyperparameter values: +# > mtry = 5 +# > splitrule = extratrees +# > min.node.size = 10

      In this case we sacrificed just 0.01 AUPR versus tuning the models. In our experience, that’s on the small side of typical. A good workflow is often to do all of your development using flash_models, and as a final step before putting a model into production, retrain the model using tune_models.

      @@ -428,24 +428,24 @@

      Faster Model Training

      Prediction

      predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

      +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:18:58 +# > Performance in training: PR = 0.9 +# > # A tibble: 692 x 15 +# > diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp +# > * <fct> <dbl> <dbl> <dbl> <dbl> +# > 1 N 0.0677 -0.843 -1.19 -0.521 +# > 2 Y 0.642 1.22 2.01 -0.686 +# > 3 N 0.00366 -0.843 -1.05 -0.521 +# > 4 Y 0.723 -1.14 0.509 -2.66 +# > 5 N 0.228 0.338 -0.175 0.138 +# > # ... with 687 more rows, and 10 more variables: skinfold <dbl>, +# > # insulin <dbl>, pedigree <dbl>, age <dbl>, weight_class_normal <dbl>, +# > # weight_class_obese <dbl>, weight_class_overweight <dbl>, +# > # weight_class_underweight <dbl>, weight_class_hcai_missing <dbl>, +# > # weight_class_other <dbl>

      To get predictions on a new dataset, pass the new data to predict, and it will automatically be prepared based on the recipe generated on the training data. We can plot the predictions to see how well our model is doing, and we see that it’s separating diabetic from non-diabetic individuals pretty well, although there a fair number of non-diabetics with high predicted probabilities of diabetes. This may be due to optimizing for precision recall, or may indicate pre-diabetic patients.

      @@ -453,44 +453,44 @@

      Prediction

      A Regression Example

      All the examples above have been classification tasks, predicting a yes/no outcome. Here’s an example of a full regression modeling pipeline on a silly problem: predicting individuals’ ages. The code is very similar to classification.

      regression_models <- machine_learn(pima_diabetes, patient_id, outcome = age)
      -#>  Training new data prep recipe
      -#>  Variable(s) ignored in prep_data won't be used to tune models: patient_id
      -#>  age looks numeric, so training regression algorithms.
      -#>  Running cross validation for Random Forest
      -#>  Running cross validation for k-Nearest Neighbors
      +# > Training new data prep recipe
      +# > Variable(s) ignored in prep_data won't be used to tune models: patient_id
      +# > age looks numeric, so training regression algorithms.
      +# > Running cross validation for Random Forest
      +# > Running cross validation for k-Nearest Neighbors
       summary(regression_models)
      -#>  Models trained: 2018-04-02 05:58:45
      -#>  
      -#>  Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values.
      -#>  Best performance: RMSE = 9.07
      -#>  By Random Forest with hyperparameters:
      -#>    mtry = 10
      -#>    splitrule = extratrees
      -#>    min.node.size = 10
      -#>  
      -#>  Out-of-fold performance of all trained models:
      -#>  
      -#>  $`Random Forest`
      -#>  # A tibble: 10 x 9
      -#>    min.node.size  mtry splitrule   RMSE Rsquared   MAE RMSESD RsquaredSD
      -#>  *         <int> <int> <fct>      <dbl>    <dbl> <dbl>  <dbl>      <dbl>
      -#>  1            10    10 extratrees  9.07    0.404  6.43  0.640     0.0358
      -#>  2             8    11 extratrees  9.09    0.402  6.43  0.626     0.0396
      -#>  3            12     5 extratrees  9.13    0.405  6.56  0.666     0.0272
      -#>  4            10    13 variance    9.33    0.376  6.60  0.633     0.0358
      -#>  5             7    10 variance    9.34    0.374  6.61  0.583     0.0303
      -#>  # ... with 5 more rows, and 1 more variable: MAESD <dbl>
      -#>  
      -#>  $`k-Nearest Neighbors`
      -#>  # A tibble: 10 x 9
      -#>     kmax distance kernel       RMSE Rsquared   MAE RMSESD RsquaredSD MAESD
      -#>  * <dbl>    <dbl> <fct>       <dbl>    <dbl> <dbl>  <dbl>      <dbl> <dbl>
      -#>  1   16.    2.60  inv          9.44    0.363  6.65  0.811     0.0649 0.551
      -#>  2   14.    1.73  gaussian     9.44    0.361  6.66  0.717     0.0593 0.452
      -#>  3   13.    1.58  triangular   9.49    0.355  6.66  0.764     0.0697 0.461
      -#>  4   10.    0.933 rectangular  9.55    0.346  6.79  0.637     0.0438 0.412
      -#>  5    6.    1.68  inv          9.64    0.340  6.74  0.723     0.0677 0.465
      -#>  # ... with 5 more rows
      +# > Models trained: 2018-04-02 16:19:14 +# > +# > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. +# > Best performance: RMSE = 9.07 +# > By Random Forest with hyperparameters: +# > mtry = 10 +# > splitrule = extratrees +# > min.node.size = 10 +# > +# > Out-of-fold performance of all trained models: +# > +# > $`Random Forest` +# > # A tibble: 10 x 9 +# > min.node.size mtry splitrule RMSE Rsquared MAE RMSESD RsquaredSD +# > * <int> <int> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> +# > 1 10 10 extratrees 9.07 0.404 6.43 0.640 0.0358 +# > 2 8 11 extratrees 9.09 0.402 6.43 0.626 0.0396 +# > 3 12 5 extratrees 9.13 0.405 6.56 0.666 0.0272 +# > 4 10 13 variance 9.33 0.376 6.60 0.633 0.0358 +# > 5 7 10 variance 9.34 0.374 6.61 0.583 0.0303 +# > # ... with 5 more rows, and 1 more variable: MAESD <dbl> +# > +# > $`k-Nearest Neighbors` +# > # A tibble: 10 x 9 +# > kmax distance kernel RMSE Rsquared MAE RMSESD RsquaredSD MAESD +# > * <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +# > 1 16. 2.60 inv 9.44 0.363 6.65 0.811 0.0649 0.551 +# > 2 14. 1.73 gaussian 9.44 0.361 6.66 0.717 0.0593 0.452 +# > 3 13. 1.58 triangular 9.49 0.355 6.66 0.764 0.0697 0.461 +# > 4 10. 0.933 rectangular 9.55 0.346 6.79 0.637 0.0438 0.412 +# > 5 6. 1.68 inv 9.64 0.340 6.74 0.723 0.0677 0.465 +# > # ... with 5 more rows

      Let’s make a prediction on a hypothetical new patient. Note that the model handles missingness in insulin and a new category level in weight_class without a problem (but warns about it).

      +# > Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. +# > weight_class: ??? +# > Prepping data based on provided recipe +# > "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:19:14 +# > Performance in training: RMSE = 9.07 +# > # A tibble: 1 x 9 +# > predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin +# > * <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> +# > 1 23.9 0. 80. 55. 24. NA +# > # ... with 3 more variables: weight_class <fct>, pedigree <dbl>, +# > # diabetes <fct> From dc89f5323b7216016d2bf3d19d54082290319606 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 16:52:02 -0600 Subject: [PATCH 8/9] remove lingering offending file --- man/figures/README-plot predictions-1.png | Bin 70796 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 man/figures/README-plot predictions-1.png diff --git a/man/figures/README-plot predictions-1.png b/man/figures/README-plot predictions-1.png deleted file mode 100644 index 13fbefd9536553afc2c338b0e604e0bef7b0bbdb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 70796 zcmbTeby!s0_dYyy2}mnQs2~lZbSr`&BHb`3-61_RC<-VjDIne5IfRrbNOyy?&SY{Pq6hbur_av(Mfu?sczwogk$bPYLm<@gWchp`7d!6$k{=7Xm?-#Knd{ zAP1jl2f+t7Eu^HBtfZbw*;(7YR<(a+Wb)L+*5tK?k;>D@5QyN%kLvp7x75kS;f`i^ z8M{@jdEYu^OAs;8Xsg}5idUqP+AGeT#FTX7;8~mGHtTyxtFk&=&@O2wuQA)ldBc| znH-HWu8@=J!Mx+Uu=ir@pWeJnrov9qRHkLu&v}l{uSnopp*thh>|roAG1oo%N>A0dylC39t`4c#~4N2(*6~VW? zm*4R3am;hR?-5w;P;DB`%Z+IJ+T_w&#iz5oD}wngs8%;_r~UP*jcVfV@*;vSuG7Q) z$=OMryDZY{b&}A=s^`kyn`lm)vy7O?0E*Ut_dY2ykC4mr7bTO=u4CM^q+XQs;R;V9 z(&hfGZ(---KEmQtUE=2H%s?Bu=GGa{4l<`6)axObC*bS$6;`all(+2H}& z9rs{S1!TmnQZnPMlhJ3^(oj)>;q$r=4^=YbrKg0-;mI#AAF@5yQyt0YAgTvo-A@Y3fk>(*M1uIkh_ z*uN>a`(oRI+GzJ2b`~^=WxS(*#x57_>x*jJaS_Sn$6%dU9&6lu@$pIie!%{_a*ldt zFP8J~^<7>aUR_?@UQPPwpG7(oemBf<+0#23d`DXDQR~cjP`cl!GFy$d%71qIt~CW6 z#8EEWH}{+kBHxuWx6EWpr4-64&~UE zsv!D}roGU$2ZU|c2)ig;<@+NHsf+m8qT>)N@3U_N{&*g0uKI4=Mu!G#YkEIyKmGxn zm`s=J>DSY1-x?%#)=#A;!B65M+;UOLf4`&rhLV0Q3u9ocyD zz|H%7aCNfPF@KrUw?aT9p>bx*X;@tHG7n<*(FZL#$x|Iek6;S|k%Y)Sd8FoswmyaR zRc+|Bedir#w7!d91{^N`SV@_@^}9@oE(sh%O_F?FW8Jo|u|NXD=OwcQ85t%AHJ;z) zdEx=oG-e|q;q2>()tJabfo}JiSIgawJ?<|m6reo#=(r5NkU#&CY{pM#wC*&&@#n|? z{4ZZJ21qLn8XD$xNys1n$l9Z!OX7L^ThRS|l_cc-;gx%!{`@qHj_FH!hB+Ym*RM(X zLauN9@%#VYj-d$)7s7Oem+AiZvLR>;(-?n$Ea^{h9rA3O?D^i+C4-+tm#oA2`(r;$ zDh5d8Ec2J8+gJAn0+#GX{MVL2(DAq=AuLUb_q(M2+!xfkaUnZ&SGEiFC#fC-XVU~# z-_kj9!mjdFOW=FDG7cPh1%(Q1Lh5Suk$i0<`DhN-PSbqVx9F^_ti#2xq+Ge2Ua<60 zWNVjYE-oxoKU=xPS!S$VmU`OL-R-h$+?OidS9Fc97K~1s7)ZoLV*g-r5b5{Nwkk61z8I%@QpRr#*`ForlziE38J)C-$xyuAecPY#iiM z;I#zDZXx6*e5=;+Yea<6jL&6#R~%m_o#*x=_qz8^`oZV*>J*uj4&0j+ZOslZt#L_(22d8nb zSn`Z_qhbVFCPgH~hlVDodN=oJA8q=>gHg2&Q$q1 z?nI4x{7W*z^9irRsj1$^Svkt9-XlxumyQczbuTu3kC><`BBU4h3{lM+QENEc?c{|1 z4*G!yRzd8t@cHTZc!kx}Bab0F^B8wApFwz5U51QKZNgw)Qo@v7vPtGrn3EdUZ7jc~ zu6WJqsS@$4?(M+&iCoB+n%~--O3(gL2{Qt|)!#f+p%hgfgbA6B;0`%HrMXy$m8@m#&p&K)J2 z2+jG7(z(Jc(-0VP%uJtjMdl;**oT`BIRpf%Mq60i>`zv+s7_B>gUF`rOAR&O&l3#g zEgzLw7Wa%2L_MkJC<< z-}LPNn4ofQb*t&#ZRDmH!cLT8P{s^@07+kMV}AbS`a(dT+I9r9VkOv=7&j>26N`l( zpW6!4M5Pjho!OAiP!^rply`KcJVxKEsRrt~ZwRalc8t3Z)V+hBHNYwokSFrUwGIl~ z5d_+Px5$=O05)N>7Yp*;m7pp49FI|&D7ksQ18whc#zz+l_5L)MmE`@}&zEuYn)(+O z!?4SfHEtRKTRr=_G!JA!_-q)F(oFnK>+xdR0_exHo6WrYc2!d@Wkb-H@BOd-`W*sB z6JiD|*dU*pbdVo+%>t?Fb@P?pltN}D2WGt~Sd>z2b*k;?g?ET9Gj&high@T@U@~RM z*=Mysqc2 zhMtYHt?cQI=&+S|ZsgUKkYkeSGf1pTr&g6JojwlWr4@E6mW#MogV}1=cwX6+ATkmx zk4;N0%UUjfsAQl<&KO)lIu6BZG-PW0X_n+gxo|GGhkmF1aQ*j6ZY{IMLvGgnHjH

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z0L@5iKiU}YZ4Zp6tW+*3cAz7>gCW=Sgz9zmVW)|5~#50BoH(ZXl)*K7q(f}os=BttcwsClD_OE+5-wez7O zNge7;bqhP`C{LZ>!|l@I0q<*rSv))LWBCsdGY@V+);&dvvm6|q8_{|&;1gI$S`Zlf zJLCFaWd-~dev5SpPXa&6)O|9INfw6G^Ja}%u(EAn85h#=GbN_sT6;Kd$0wnBZ|XOffv>6e zJ&xbmy#~1ey3STbX5aH@D-$5^dBhaZa?DdL^tHQ*hJNtg5MU@Nayt*_7oir=+=L=h z7EVzC2?71Kq4FgY9a;+37uUW)tiC-ClH&bVt^BTyGC(xeT zA~JVwOZI-#)J!mbWh+3X!Fm1H9l-4Pi%lJko}SJN7Vl;KYvn*&k^mFGo00EfKj~LJ zqycBE+`l#MIv`KZmQoi~%0>Ux(nJVi3VpH3!CZ5P_G@!-`$&~+nAK{VO-qYZ$X091%|X$ z=I^IR;RvVvor~*4={{pi8 Bg}(p* From 4373f7004ef93f910aaae8a6a4ba950e64257f18 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 2 Apr 2018 18:39:30 -0600 Subject: [PATCH 9/9] trim rows from machine_learn example for speed --- R/machine_learn.R | 4 +- docs/dev/articles/healthcareai.html | 12 +-- docs/dev/index.html | 8 +- .../figures/README-plot_predictions-1.png | Bin 28348 -> 68176 bytes docs/dev/reference/machine_learn.html | 85 +++++++++--------- docs/dev/reference/pivot.html | 28 +++--- .../reference/plot.hcai_predicted_df-1.png | Bin 58744 -> 58650 bytes .../reference/plot.hcai_predicted_df-2.png | Bin 86624 -> 86809 bytes docs/dev/reference/plot.model_list-1.png | Bin 57667 -> 57584 bytes docs/dev/reference/predict.model_list-1.png | Bin 58167 -> 56191 bytes docs/dev/reference/predict.model_list.html | 14 +-- docs/dev/reference/prep_data.html | 20 ++--- docs/dev/reference/split_train_test.html | 14 +-- man/figures/README-plot_predictions-1.png | Bin 28348 -> 68176 bytes man/machine_learn.Rd | 4 +- 15 files changed, 94 insertions(+), 95 deletions(-) diff --git a/R/machine_learn.R b/R/machine_learn.R index 88098c317..498fc56a8 100644 --- a/R/machine_learn.R +++ b/R/machine_learn.R @@ -30,8 +30,8 @@ #' wraps. For finer control of model tuning use \code{\link{tune_models}}. #' #' @examples -#' # Split the data into training and test sets -#' d <- split_train_test(d = pima_diabetes, +#' # Split the data into training and test sets, using just 100 rows for speed +#' d <- split_train_test(d = pima_diabetes[1:100, ], #' outcome = diabetes, #' percent_train = .9) #' diff --git a/docs/dev/articles/healthcareai.html b/docs/dev/articles/healthcareai.html index 3e841489c..1fcf04f99 100644 --- a/docs/dev/articles/healthcareai.html +++ b/docs/dev/articles/healthcareai.html @@ -133,7 +133,7 @@

      # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 16:54:45 +# > Models Trained: 2018-04-02 18:37:21 # > # > Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. # > Best model: Random Forest @@ -146,7 +146,7 @@

      Now that we have our models, we can make predictions using the predict function. If you provide a new data frame to predict it will make predictions on the new data; otherwise, it will make predictions on the training data.

      predictions <- predict(quick_models)
       predictions
      -# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:54:45
      +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 18:37:21
       # > Performance in training: ROC = 0.84
       # > # A tibble: 768 x 14
       # >   diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
      @@ -236,7 +236,7 @@ 

      # > Performance Metric: PR # > Number of Observations: 692 # > Number of Features: 13 -# > Models Trained: 2018-04-02 16:55:12 +# > Models Trained: 2018-04-02 18:37:46 # > # > Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. # > Best model: Random Forest @@ -253,7 +253,7 @@

      Prediction

      predict will automatically use the best-performing model from training (evaluated out-of-fold in cross validation). If no new data is passed to predict it will make predictions on the training dataset. The predicted probabilities appear in the predicted_diabetes column.

      predict(models)
      -# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:55:09
      +# > "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 18:37:43
       # > Performance in training: PR = 0.9
       # > # A tibble: 692 x 15
       # >   diabetes predicted_diabetes pregnancies plasma_glucose diastolic_bp
      @@ -285,7 +285,7 @@ 

      # > Running cross validation for Random Forest # > Running cross validation for k-Nearest Neighbors summary(regression_models) -# > Models trained: 2018-04-02 16:55:25 +# > Models trained: 2018-04-02 18:37:59 # > # > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. # > Best performance: RMSE = 9.07 @@ -331,7 +331,7 @@

      # > Warning in ready_with_prep(object, newdata, mi): The following variables(s) had the following value(s) in predict that were not observed in training. # > weight_class: ??? # > Prepping data based on provided recipe -# > "predicted_age" predicted by Random Forest last trained: 2018-04-02 16:55:25 +# > "predicted_age" predicted by Random Forest last trained: 2018-04-02 18:37:59 # > Performance in training: RMSE = 9.07 # > # A tibble: 1 x 9 # > predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin diff --git a/docs/dev/index.html b/docs/dev/index.html index 913bfac23..e0c00f37d 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -127,15 +127,15 @@

      # > Performance Metric: ROC # > Number of Observations: 768 # > Number of Features: 12 -# > Models Trained: 2018-04-02 16:53:44 +# > Models Trained: 2018-04-02 18:36:14 # > -# > Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. +# > Models tuned via 5-fold cross validation over 9 combinations of hyperparameter values. # > Best model: Random Forest # > ROC = 0.85 # > Optimal hyperparameter values: -# > mtry = 4 +# > mtry = 5 # > splitrule = extratrees -# > min.node.size = 18

      +# > min.node.size = 11

      Make predictions and examine predictive performance:

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z|GdQqZ2*GICsJ*f=oCps0r}_A0?u%p=@ll}O8-2?{sd UmM1*zKmvcVlCLDn#0>rZ4?xF_qyPW_ diff --git a/docs/dev/reference/machine_learn.html b/docs/dev/reference/machine_learn.html index 5568f4861..7b864cf2a 100644 --- a/docs/dev/reference/machine_learn.html +++ b/docs/dev/reference/machine_learn.html @@ -192,8 +192,8 @@

      Details

      Examples

      -
      # Split the data into training and test sets -d <- split_train_test(d = pima_diabetes, +
      # Split the data into training and test sets, using just 100 rows for speed +d <- split_train_test(d = pima_diabetes[1:100, ], outcome = diabetes, percent_train = .9) @@ -207,57 +207,56 @@

      Examp #> Target: diabetes #> Class: Classification #> Performance Metric: ROC -#> Number of Observations: 692 +#> Number of Observations: 91 #> Number of Features: 12 -#> Models Trained: 2018-04-02 17:06:20 +#> Models Trained: 2018-04-02 18:36:30 #> #> Models tuned via 5-fold cross validation over 10 combinations of hyperparameter values. #> Best model: Random Forest -#> ROC = 0.83 +#> ROC = 0.77 #> Optimal hyperparameter values: -#> mtry = 3 -#> splitrule = extratrees -#> min.node.size = 18

      +#> mtry = 4 +#> splitrule = gini +#> min.node.size = 9
      # Make predictions (predicted probability of diabetes) on test data -predict(diabetes_models, d$test)
      #> Prepping data based on provided recipe
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 17:06:20 -#> Performance in training: ROC = 0.83
      #> # A tibble: 76 x 11 -#> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp -#> * <chr> <dbl> <int> <int> <int> <int> -#> 1 Y 0.668 1 6 148 72 -#> 2 Y 0.755 12 10 168 74 -#> 3 Y 0.854 23 7 196 90 -#> 4 N 0.331 29 13 145 82 -#> 5 N 0.632 41 3 180 64 -#> 6 Y 0.690 46 0 180 66 -#> 7 Y 0.456 67 0 109 88 -#> 8 N 0.308 74 4 129 86 -#> 9 N 0.116 80 2 112 66 -#> 10 N 0.317 83 7 83 78 -#> # ... with 66 more rows, and 5 more variables: skinfold <int>, insulin <int>, -#> # weight_class <chr>, pedigree <dbl>, age <int>
      +predict(diabetes_models, d$test)
      #> Prepping data based on provided recipe
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 18:36:30 +#> Performance in training: ROC = 0.77
      #> # A tibble: 9 x 11 +#> diabetes predicted_diabet… patient_id pregnancies plasma_glucose diastolic_bp +#> * <chr> <dbl> <int> <int> <int> <int> +#> 1 Y 0.196 7 3 78 50 +#> 2 Y 0.567 17 0 118 84 +#> 3 N 0.581 31 5 109 75 +#> 4 N 0.478 36 4 103 60 +#> 5 N 0.335 47 1 146 56 +#> 6 N 0.189 70 4 146 85 +#> 7 Y 0.379 73 13 126 90 +#> 8 N 0.377 78 5 95 72 +#> 9 N 0.176 83 7 83 78 +#> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, +#> # pedigree <dbl>, age <int>
      ### Regression ### # If the outcome variable is numeric, regression models will be trained -age_model <- machine_learn(d$train, patient_id, outcome = age)
      #> Training new data prep recipe
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> age looks numeric, so training regression algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      +age_model <- machine_learn(d$train, patient_id, outcome = age)
      #> Training new data prep recipe
      #> Variable(s) ignored in prep_data won't be used to tune models: patient_id
      #> age looks numeric, so training regression algorithms.
      #> Running cross validation for Random Forest
      #> Running cross validation for k-Nearest Neighbors
      # If new data isn't specifed, get predictions on training data -predict(age_model)
      #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 17:06:32 -#> Performance in training: RMSE = 8.91
      #> # A tibble: 692 x 16 +predict(age_model)
      #> "predicted_age" predicted by Random Forest last trained: 2018-04-02 18:36:34 +#> Performance in training: RMSE = 8.84
      #> # A tibble: 91 x 16 #> age predicted_age pregnancies plasma_glucose diastolic_bp skinfold insulin #> * <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> -#> 1 31 25.9 1 85. 66.0 29.0 156. -#> 2 32 38.5 8 183. 64.0 29.3 156. -#> 3 21 23.7 1 89. 66.0 23.0 94.0 -#> 4 33 29.0 0 137. 40.0 35.0 168. -#> 5 30 39.1 5 116. 74.0 29.3 156. -#> 6 26 28.2 3 78. 50.0 32.0 88.0 -#> 7 29 39.5 10 115. 72.4 29.3 156. -#> 8 53 36.6 2 197. 70.0 45.0 543. -#> 9 54 43.7 8 125. 96.0 29.3 156. -#> 10 30 33.9 4 110. 92.0 29.3 156. -#> # ... with 682 more rows, and 9 more variables: pedigree <dbl>, +#> 1 50 43.0 6 148. 72.0 35.0 169. +#> 2 31 25.4 1 85. 66.0 29.0 169. +#> 3 32 41.5 8 183. 64.0 29.0 169. +#> 4 21 24.3 1 89. 66.0 23.0 94.0 +#> 5 33 33.0 0 137. 40.0 35.0 168. +#> 6 30 31.4 5 116. 74.0 29.0 169. +#> 7 29 37.6 10 115. 72.4 29.0 169. +#> 8 53 47.7 2 197. 70.0 45.0 543. +#> 9 54 45.6 8 125. 96.0 29.0 169. +#> 10 30 33.2 4 110. 92.0 29.0 169. +#> # ... with 81 more rows, and 9 more variables: pedigree <dbl>, #> # weight_class_normal <dbl>, weight_class_obese <dbl>, -#> # weight_class_overweight <dbl>, weight_class_other <dbl>, -#> # weight_class_hcai_missing <dbl>, diabetes_Y <dbl>, diabetes_other <dbl>, +#> # weight_class_overweight <dbl>, weight_class_hcai_missing <dbl>, +#> # weight_class_other <dbl>, diabetes_Y <dbl>, diabetes_other <dbl>, #> # diabetes_hcai_missing <dbl>
      ### Faster model training without tuning hyperparameters ### @@ -268,13 +267,13 @@

      Examp #> Target: diabetes #> Class: Classification #> Performance Metric: ROC -#> Number of Observations: 692 +#> Number of Observations: 91 #> Number of Features: 12 -#> Models Trained: 2018-04-02 17:06:34 +#> Models Trained: 2018-04-02 18:36:36 #> #> Models have not been tuned. Performance estimated via 5-fold cross validation at fixed hyperparameter values. #> Best model: Random Forest -#> ROC = 0.84 +#> ROC = 0.77 #> User-selected hyperparameter values: #> mtry = 5 #> splitrule = extratrees diff --git a/docs/dev/reference/pivot.html b/docs/dev/reference/pivot.html index a458f6444..7908fa2b1 100644 --- a/docs/dev/reference/pivot.html +++ b/docs/dev/reference/pivot.html @@ -226,26 +226,26 @@

      Examp bills

      #> # A tibble: 8 x 4 #> patient_id dept_id charge date #> <chr> <chr> <dbl> <date> -#> 1 A ED 5642. 2024-12-25 -#> 2 A ICU 6766. 2024-12-24 -#> 3 A ED 4100. 2024-12-24 -#> 4 A ICU 826. 2024-12-23 -#> 5 B ED 8096. 2024-12-24 -#> 6 B ICU 5430. 2024-12-25 -#> 7 B ED 1097. 2024-12-25 -#> 8 B ICU 3855. 2024-12-25
      +#> 1 A ED 2410. 2024-12-23 +#> 2 A ICU 9265. 2024-12-24 +#> 3 A ED 118. 2024-12-24 +#> 4 A ICU 1650. 2024-12-24 +#> 5 B ED 3184. 2024-12-25 +#> 6 B ICU 4829. 2024-12-24 +#> 7 B ED 196. 2024-12-23 +#> 8 B ICU 2433. 2024-12-25
      # Total charges per patient x department: pivot(bills, patient_id, dept_id, charge, sum)
      #> # A tibble: 2 x 3 #> patient_id dept_id_ED dept_id_ICU #> <fct> <dbl> <dbl> -#> 1 A 9741. 7592. -#> 2 B 9193. 9285.
      +#> 1 A 2528. 10915. +#> 2 B 3381. 7262.
      # Count of charges per patient x day: pivot(bills, patient_id, date)
      #> No fill column was provided, so using "1" for present entities
      #> There are rows that contain the same values of both patient_id and date but you didn't provide a function to 'fun' for their aggregation. Proceeding with the default: fun = sum.
      #> # A tibble: 2 x 4 #> patient_id `date_2024-12-23` `date_2024-12-24` `date_2024-12-25` #> <fct> <int> <int> <int> -#> 1 A 1 2 1 -#> 2 B NA 1 3
      +#> 1 A 1 3 NA +#> 2 B 1 1 2
      # Can provide a custom function to fun, which will take fill as input. # Get the difference between the greatest and smallest charge in each # department for each patient and format it as currency. @@ -257,8 +257,8 @@

      Examp )

      #> # A tibble: 2 x 3 #> patient_id dept_id_ED dept_id_ICU #> <fct> <chr> <chr> -#> 1 A $1541.9 $5939.53 -#> 2 B $6998.77 $1574.71
      +#> 1 A $2292.21 $7614.78 +#> 2 B $2987.89 $2395.96
      #> Prepping data based on provided recipe
      predictions
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 16:54:11 -#> Performance in training: ROC = 0.72
      #> # A tibble: 5 x 11 +predictions <- predict(models, newdata = pima_diabetes[21:25, ])
      #> Prepping data based on provided recipe
      predictions
      #> "predicted_diabetes" predicted by Random Forest last trained: 2018-04-02 18:36:45 +#> Performance in training: ROC = 0.8
      #> # A tibble: 5 x 11 #> diabetes predicted_diabet… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> -#> 1 N 0.768 21 3 126 88 -#> 2 N 0.803 22 8 99 84 -#> 3 Y 0.894 23 7 196 90 -#> 4 Y 0.401 24 9 119 80 -#> 5 Y 0.709 25 11 143 94 +#> 1 N 0.727 21 3 126 88 +#> 2 N 0.781 22 8 99 84 +#> 3 Y 0.932 23 7 196 90 +#> 4 Y 0.522 24 9 119 80 +#> 5 Y 0.769 25 11 143 94 #> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, #> # pedigree <dbl>, age <int>
      plot(predictions)
      diff --git a/docs/dev/reference/prep_data.html b/docs/dev/reference/prep_data.html index 876baf94b..0cb7b19fd 100644 --- a/docs/dev/reference/prep_data.html +++ b/docs/dev/reference/prep_data.html @@ -354,16 +354,16 @@

      Examp #> Adding levels to: other, hcai_missing [trained]
      #> Current data:
      #> # A tibble: 700 x 10 #> patient_id pregnancies plasma_glucose diastolic_bp skinfold insulin #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.646 0.872 -0.0200 0.632 0.341 -#> 2 2 -0.840 -1.19 -0.513 0.00954 -0.868 -#> 3 3 1.24 2.02 -0.677 -0.740 0.876 -#> 4 4 -0.840 -1.06 -0.513 -0.613 -0.597 -#> 5 5 -1.14 0.512 -2.65 0.632 0.153 -#> 6 6 0.349 -0.175 0.144 -0.289 -0.335 -#> 7 7 -0.246 -1.42 -1.83 0.321 -0.658 -#> 8 8 1.83 -0.208 -0.0784 0.449 -0.299 -#> 9 9 -0.543 2.47 -0.184 1.67 3.96 -#> 10 10 1.24 0.119 1.95 0.362 0.813 +#> 1 1 0.646 0.872 -0.0197 0.636 0.659 +#> 2 2 -0.840 -1.19 -0.513 0.0132 -0.853 +#> 3 3 1.24 2.02 -0.677 -0.889 0.724 +#> 4 4 -0.840 -1.06 -0.513 -0.609 -0.608 +#> 5 5 -1.14 0.512 -2.65 0.636 0.144 +#> 6 6 0.349 -0.175 0.145 -0.377 -0.275 +#> 7 7 -0.246 -1.42 -1.83 0.324 -0.669 +#> 8 8 1.83 -0.208 0.114 0.532 -0.260 +#> 9 9 -0.543 2.47 -0.184 1.67 3.95 +#> 10 10 1.24 0.119 1.95 0.347 0.581 #> # ... with 690 more rows, and 4 more variables: weight_class <fct>, #> # pedigree <dbl>, age <dbl>, diabetes <fct>
      diff --git a/docs/dev/reference/split_train_test.html b/docs/dev/reference/split_train_test.html index 5a77c038c..3df7d9d17 100644 --- a/docs/dev/reference/split_train_test.html +++ b/docs/dev/reference/split_train_test.html @@ -171,7 +171,6 @@

      Examp #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 -#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 @@ -179,10 +178,8 @@

      Examp #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 -#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 -#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 @@ -191,16 +188,19 @@

      Examp #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 +#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 +#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 +#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> #> $test -#> mpg cyl disp hp drat wt qsec vs am gear carb -#> Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 -#> Ford Pantera L 15.8 8 351 264 4.22 3.170 14.5 0 1 5 4 -#> Ferrari Dino 19.7 6 145 175 3.62 2.770 15.5 0 1 5 6 +#> mpg cyl disp hp drat wt qsec vs am gear carb +#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 +#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 +#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #>

      ppercent_train

      Proportion of rows in d to put into training. Default is 0.8