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最近邻

k 最近邻分类

要训练 k 最近邻模型,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 fitcknn 训练 k 最近邻模型。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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fitcknnFit k-nearest neighbor classifier
ExhaustiveSearcherCreate exhaustive nearest neighbor searcher
KDTreeSearcherCreate Kd-tree nearest neighbor searcher
creatensCreate nearest neighbor searcher object
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values
crossvalCross-validate machine learning model
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
lossLoss of k-nearest neighbor classifier
resubLossResubstitution classification loss
compareHoldoutCompare accuracies of two classification models using new data
edgeEdge of k-nearest neighbor classifier
marginMargin of k-nearest neighbor classifier
resubEdgeResubstitution classification edge
resubMarginResubstitution classification margin
testckfoldCompare accuracies of two classification models by repeated cross-validation
predictPredict labels using k-nearest neighbor classification model
resubPredictClassify training data using trained classifier
gatherGather properties of Statistics and Machine Learning Toolbox object from GPU
pdist成对观测值之间的两两距离
pdist2Pairwise distance between two sets of observations

对象

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ClassificationKNNk-nearest neighbor classification
ClassificationPartitionedModelCross-validated classification model

主题

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