noteJ

noteJ

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Pre-processing MLP calculation Automated Test data-mining Naïve Bayes classifier association rules Apriori algorithm Dimensionality Reduction Numeric underflow feature subset selection stratified sampling multiple classification Curse of dimensionality discretization pre processing variable transformation LVQ zero count Nearest Neighbor Classifier feature creation

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roc(1)

  • What is ROC?

    ROC stands for Receiver Operating Characteristic. It is a graphical representation that illustrates the performance of a binary classification model. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The true positive rate (TPR) is also called sensitivity, recall or hit rate and it is the proportion of actual positive cases that..

    2023.03.07
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