Generalized Additive Model
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
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.
Create GAM Object
|Local interpretable model-agnostic explanations (LIME)|
|Compute partial dependence|
|Plot local effects of terms in generalized additive model (GAM)|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
Assess Predictive Performance on New Observations
Assess Predictive Performance on Training Data
Assess Predictive Performance on Cross-Validated Data
|Classify observations in cross-validated classification model|
|Classification loss for cross-validated classification model|
|Classification margins for cross-validated classification model|
|Classification edge for cross-validated classification model|
|Cross-validate function for classification|
- Train Generalized Additive Model for Binary Classification
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.