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广义加性模型

用于二类分类的由一元和二元形状函数组成的可解释模型

使用 fitcgam 拟合二类分类的广义加性模型。

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类分数(类概率的对数几率)。fitcgam 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(分类分数)的贡献相互独立,因此该模型易于解释。

对象

ClassificationGAMGeneralized additive model (GAM) for binary classification
CompactClassificationGAMCompact generalized additive model (GAM) for binary classification
ClassificationPartitionedGAMCross-validated generalized additive model (GAM) for classification

函数

全部展开

fitcgamFit generalized additive model (GAM) for binary classification
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
predictClassify observations using generalized additive model (GAM)
lossClassification loss for generalized additive model (GAM)
marginClassification margins for generalized additive model (GAM)
edgeClassification edge for generalized additive model (GAM)
resubPredictClassify training data using trained classifier
resubLossResubstitution classification loss
resubMarginResubstitution classification margin
resubEdgeResubstitution classification edge
kfoldPredictClassify observations in cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldMarginClassification margins for cross-validated classification model
kfoldEdgeClassification edge for cross-validated classification model
kfoldfunCross-validate function for classification
compareHoldoutCompare accuracies of two classification models using new data
testckfoldCompare accuracies of two classification models by repeated cross-validation

主题

Train Generalized Additive Model for Binary Classification

Train a generalized additive model (GAM) with optimal parameters, assess predictive performance, and interpret the trained model.