noteJ

noteJ

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

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sensitivity(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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