Learner's World

In continuation of my previous posts on various Performance measures for classifiers, here, I've explained the concept of single score measure namely; 'F - score'. In my previous posts, I had discussed four fundamental numbers, namely, true positive, true negative, false positive and false negative and eight basic ratios, namely, sensitivity(or recall or true positive rate) & specificity (or true negative rate), false positive rate (or type-I error) & false negative rates (or type-II error), positive predicted value (or precision) & negative predicted value, and false discovery rate (or q-value) & false omission rate. I had also discussed accuracy paradox, the relationship between various basic ratios and their trade-off to evaluate the performance of a classifier with examples. I'll be using the same confusion matrix for reference. Precision & Recall: First let's briefly revisit the understanding of'Precision (PPV) & Recall (sensitivity)'.