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判别分析

正则化线性判别分析和二次判别分析

要以交互方式训练判别分析模型,可以使用分类学习器。为了获得更大的灵活性,可以在命令行界面中使用 fitcdiscr 来训练判别分析模型。训练模型后,可将模型和预测变量数据传递给 predict,以预测标签或估计后验概率。

App

分类学习器使用有监督的机器学习训练模型以对数据进行分类

函数

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fitcdiscrFit discriminant analysis classifier
makecdiscrConstruct discriminant analysis classifier from parameters
compactCompact discriminant analysis classifier
cvshrinkCross-validate regularization of linear discriminant
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values
crossvalCross-validated discriminant analysis classifier
kfoldEdgeClassification edge for cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldfunCross-validate function for classification
kfoldMarginClassification margins for cross-validated classification model
kfoldPredictClassify observations in cross-validated classification model
lossClassification error
resubLossClassification error by resubstitution
logpLog unconditional probability density for discriminant analysis classifier
mahalMahalanobis distance to class means of discriminant analysis classifier
nLinearCoeffsNumber of nonzero linear coefficients
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge
marginClassification margins
resubEdgeClassification edge by resubstitution
resubMarginClassification margins by resubstitution
testckfoldCompare accuracies of two classification models by repeated cross-validation
predictPredict labels using discriminant analysis classification model
resubPredictPredict resubstitution labels of discriminant analysis classification model
classifyClassify observations using discriminant analysis

ClassificationDiscriminantDiscriminant analysis classification
CompactClassificationDiscriminantCompact discriminant analysis class
ClassificationPartitionedModelCross-validated classification model

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