Generalized Additive Model
Interpretable model composed of univariate and bivariate shape functions for regression
Usefitrgam
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
Topics
- Train Generalized Additive Model for Regression
Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.