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C7 - MLWR2 - Neural Network & SVM.r
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C7 - MLWR2 - Neural Network & SVM.r
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##### Chapter 7: Neural Networks and Support Vector Machines -------------------
##### Part 1: Neural Networks -------------------
## Example: Modeling the Strength of Concrete ----
## Step 2: Exploring and preparing the data ----
# read in data and examine structure
concrete <- read.csv("concrete.csv")
str(concrete)
# custom normalization function
normalize <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
# apply normalization to entire data frame
concrete_norm <- as.data.frame(lapply(concrete, normalize))
# confirm that the range is now between zero and one
summary(concrete_norm$strength)
# compared to the original minimum and maximum
summary(concrete$strength)
# create training and test data
concrete_train <- concrete_norm[1:773, ]
concrete_test <- concrete_norm[774:1030, ]
## Step 3: Training a model on the data ----
# train the neuralnet model
library(neuralnet)
# simple ANN with only a single hidden neuron
set.seed(12345) # to guarantee repeatable results
concrete_model <- neuralnet(formula = strength ~ cement + slag +
ash + water + superplastic +
coarseagg + fineagg + age,
data = concrete_train)
# visualize the network topology
plot(concrete_model)
## Step 4: Evaluating model performance ----
# obtain model results
model_results <- compute(concrete_model, concrete_test[1:8])
# obtain predicted strength values
predicted_strength <- model_results$net.result
# examine the correlation between predicted and actual values
cor(predicted_strength, concrete_test$strength)
## Step 5: Improving model performance ----
# a more complex neural network topology with 5 hidden neurons
set.seed(12345) # to guarantee repeatable results
concrete_model2 <- neuralnet(strength ~ cement + slag +
ash + water + superplastic +
coarseagg + fineagg + age,
data = concrete_train, hidden = 5)
# plot the network
plot(concrete_model2)
# evaluate the results as we did before
model_results2 <- compute(concrete_model2, concrete_test[1:8])
predicted_strength2 <- model_results2$net.result
cor(predicted_strength2, concrete_test$strength)
##### Part 2: Support Vector Machines -------------------
## Example: Optical Character Recognition ----
## Step 2: Exploring and preparing the data ----
# read in data and examine structure
letters <- read.csv("letterdata.csv")
str(letters)
# divide into training and test data
letters_train <- letters[1:16000, ]
letters_test <- letters[16001:20000, ]
## Step 3: Training a model on the data ----
# begin by training a simple linear SVM
library(kernlab)
letter_classifier <- ksvm(letter ~ ., data = letters_train,
kernel = "vanilladot")
# look at basic information about the model
letter_classifier
## Step 4: Evaluating model performance ----
# predictions on testing dataset
letter_predictions <- predict(letter_classifier, letters_test)
head(letter_predictions)
table(letter_predictions, letters_test$letter)
# look only at agreement vs. non-agreement
# construct a vector of TRUE/FALSE indicating correct/incorrect predictions
agreement <- letter_predictions == letters_test$letter
table(agreement)
prop.table(table(agreement))
## Step 5: Improving model performance ----
set.seed(12345)
letter_classifier_rbf <- ksvm(letter ~ ., data = letters_train, kernel = "rbfdot")
letter_predictions_rbf <- predict(letter_classifier_rbf, letters_test)
agreement_rbf <- letter_predictions_rbf == letters_test$letter
table(agreement_rbf)
prop.table(table(agreement_rbf))