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Generalized Additive Model

Interpretable model composed of univariate and bivariate shape functions for regression

Usefitrgamto 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.fitrgamuses 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 预测反应cross-valida观测ted regression model
kfoldLoss Loss for cross-validated partitioned regression model
kfoldfun Cross-validate function for regression

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