Model Building and Assessment

Feature selection, cross validation, predictive performance evaluation, classification accuracy comparison


Classification Learner Train models to classify data using supervised machine learning


sequentialfs Sequential feature selection
relieff Importance of attributes (predictors) using ReliefF algorithm
crossval Loss estimate using cross validation
confusionmat Confusion matrix
perfcurve Receiver operating characteristic (ROC) curve or other performance curve for classifier output
testcholdout Compare predictive accuracies of two classification models
testckfold Compare accuracies of two classification models by repeated cross validation


cvpartition Data partitions for cross validation
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