noteJ

noteJ

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

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specificity(1)

  • What is trade-off between sensitivity (or TPR) and specificity (1-FPR)?

    In binary classification problems, sensitivity (also called true positive rate or TPR) and specificity (also called true negative rate or TNR) are two common performance metrics used to evaluate the performance of a classification model. Sensitivity measures the proportion of true positives (correctly identified positives) out of all actual positives. It is defined as: TPR = TP / (TP + FN) where..

    2023.03.07
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