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广义加性模型

用于回归的由一元和二元形状函数组成的可解释模型

使用 fitrgam 拟合用于回归的广义加性模型。

广义加性模型 (GAM) 是一种可解释的模型，它使用预测变量的一元和二元形状函数之和来解释响应变量。fitrgam 使用提升树作为每个预测变量以及可选的每对预测变量的形状函数；因此，该函数可以捕获预测变量和响应变量之间的非线性关系。由于单个形状函数对预测（响应值）的贡献相互独立，因此该模型易于解释。

对象

RegressionGAMGeneralized additive model (GAM) for regression
CompactRegressionGAMCompact generalized additive model (GAM) for regression
RegressionPartitionedGAMCross-validated generalized additive model (GAM) for regression

函数

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fitrgamFit generalized additive model (GAM) for regression
compactReduce size of machine learning model
crossvalCross-validate machine learning model
addInteractionsAdd interaction terms to univariate generalized additive model (GAM)
resumeResume training of generalized additive model (GAM)
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotLocalEffectsPlot local effects of terms in generalized additive model (GAM)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values
predictPredict responses using generalized additive model (GAM)
lossRegression loss for generalized additive model (GAM)
resubPredictPredict responses for training data using trained regression model
resubLossResubstitution regression loss
kfoldPredictPredict responses for observations in cross-validated regression model
kfoldLossLoss for cross-validated partitioned regression model
kfoldfunCross-validate function for regression

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