Interpretable model composed of univariate and bivariate shape functions for regression

Use `fitrgam` to fit a generalized additive model for regression.

A generalized additive model (GAM) is an interpretable model that explains a response variable using a sum of univariate and bivariate shape functions of predictors. `fitrgam` uses a boosted tree as a shape function for each predictor and, optionally, each pair of predictors; therefore, the function can capture a nonlinear relation between a predictor and the response variable. Because contributions of individual shape functions to the prediction (response value) are well separated, the model is easy to interpret.

## Objects

 `RegressionGAM` Generalized additive model (GAM) for regression `CompactRegressionGAM` Compact generalized additive model (GAM) for regression `RegressionPartitionedGAM` Cross-validated generalized additive model (GAM) for regression

## Functions

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 `fitrgam` Fit generalized additive model (GAM) for regression `compact` Reduce size of machine learning model `crossval` Cross-validate machine learning model
 `addInteractions` Add interaction terms to univariate generalized additive model (GAM) `resume` Resume training of generalized additive model (GAM)
 `lime` Local interpretable model-agnostic explanations (LIME) `partialDependence` Compute partial dependence `plotLocalEffects` Plot local effects of terms in generalized additive model (GAM) `plotPartialDependence` Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots `shapley` Shapley values
 `predict` Predict responses using generalized additive model (GAM) `loss` Regression loss for generalized additive model (GAM)
 `resubPredict` Predict responses for training data using trained regression model `resubLoss` Resubstitution regression loss
 `kfoldPredict` Predict responses for observations in cross-validated regression model `kfoldLoss` Loss for cross-validated partitioned regression model `kfoldfun` Cross-validate function for regression

## Topics

Train Generalized Additive Model for Regression

Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.
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