Lone Peak

Added

• Support for models of outcomes with more than two classes (multiclass).
• Explore a model's logic with explore. Make counterfactual predictions across the most-important features in a model to see how those features influence predicted outcomes.
  • plot method to visualize a model's logic.
• Identify opportunities to improve a patient's outcome with Patient Impact Predictor, pip. Carefully specify variables and alternative values that exert causal influence on outcomes; then get recommended actions for a given patient with expected outcomes given the actions.
• Predict outcome groups based on how bad false alarms are relative to missed detections (outcome_groups argument to predict).
• Group predictions into risk groups using the risk_groups argument to predict.
  • plot support for outcome- and risk-group predictions.
• Get thresholds to split outcome classes to optimize various performance metrics with get_thresholds.
  • plot method to compare performance across metrics at various thresholds.
• split_train_test can keep multiple observations of an individual in the same split via the grouping_col argument.
• Replace values that represent missingness but have been interpreted by R as strings with NA with make_na.
  • If missingness finds any such strings it issues a warning with code that can be used to do the replacement.
• Add counts to factor levels with rename_with_counts.
• summary.missingness method for wide datasets with missingness in many columns.

Changed

• In prep_data, trigonometric transformations make circular features out of dates and times for more informative features in less-wide data frames.
• Fixed AUPR in plot.model_class and summary.model_class.
• Can specify performance metric to optimize in machine_learn.
• missingness is faster.
• Regression prediction plots are plotted at 1:1 aspect ratio.
• add_best_levels works in deployment even if none of the columns to be created are present in the deployment observations.
• prep_data can handle logical features.
• outcome doesn't need to be re-declared in model training if it was specified in data prep.

Removed

• No longer support training models on un-prepped data.
• No longer support wrapping caret-trained models into a model_list.