Generalized Additive Model

Interpretable model composed of univariate and bivariate shape functions for binary classification

Use fitcgam to fit a generalized additive model for binary classification.

A generalized additive model (GAM) is an interpretable model that explains class scores (the logit of class probabilities) using a sum of univariate and bivariate shape functions of predictors. fitcgam 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 (classification score) are well separated, the model is easy to interpret.

Objects

`ClassificationGAM`	Generalized additive model (GAM) for binary classification
`CompactClassificationGAM`	Compact generalized additive model (GAM) for binary classification
`ClassificationPartitionedGAM`	Cross-validated generalized additive model (GAM) for classification

Functions

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Create GAM Object

`fitcgam`	Fit generalized additive model (GAM) for binary classification
`compact`	Reduce size of machine learning model
`crossval`	Cross-validate machine learning model

Update GAM

`addInteractions`	Add interaction terms to univariate generalized additive model (GAM)
`resume`	Resume training of generalized additive model (GAM)

Interpret Prediction

`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

Assess Predictive Performance on New Observations

`predict`	Classify observations using generalized additive model (GAM)
`loss`	Classification loss for generalized additive model (GAM)
`margin`	Classification margins for generalized additive model (GAM)
`edge`	Classification edge for generalized additive model (GAM)

Assess Predictive Performance on Training Data

`resubPredict`	Classify training data using trained classifier
`resubLoss`	Resubstitution classification loss
`resubMargin`	Resubstitution classification margin
`resubEdge`	Resubstitution classification edge

Assess Predictive Performance on Cross-Validated Data

`kfoldPredict`	Classify observations in cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldfun`	Cross-validate function for classification

Compare Accuracies

`compareHoldout`	Compare accuracies of two classification models using new data
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

Topics

Train Generalized Additive Model for Binary Classification
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.