fitrgam to fit a generalized additive model for
A generalized additive model (GAM) is an interpretable model that explains a
response variable using a sum of univariate and bivariate shape functions of
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.
|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|
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.