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可解释性

训练可解释的分类模型和解释复杂的分类模型

使用本质上可解释的分类模型,如线性模型、决策树和广义加性模型,或使用可解释特性来解释本质上不可解释的复波分类模型。

要了解如何解释分类模型,请参阅 Interpret Machine Learning Models

函数

全部展开

与模型无关的局部可解释性解释 (LIME)

limeLocal interpretable model-agnostic explanations (LIME) (自 R2020b 起)
fitFit simple model of local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
plotPlot results of local interpretable model-agnostic explanations (LIME) (自 R2020b 起)

夏普利值

shapleyShapley values (自 R2021a 起)
fitCompute Shapley values for query points (自 R2021a 起)
plotPlot Shapley values using bar graphs (自 R2021a 起)
boxchartVisualize Shapley values using box charts (box plots) (自 R2024a 起)
swarmchartVisualize Shapley values using swarm scatter charts (自 R2024a 起)

部分依赖

partialDependenceCompute partial dependence (自 R2020b 起)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
fitcgamFit generalized additive model (GAM) for binary classification (自 R2021a 起)
fitclinearFit binary linear classifier to high-dimensional data
fitctreeFit binary decision tree for multiclass classification

对象

ClassificationGAMGeneralized additive model (GAM) for binary classification (自 R2021a 起)
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationTreeBinary decision tree for multiclass classification

主题

模型解释

可解释模型