Choosing the Right Metric for Evaluating Machine Learning Models — Part 2

Second part of the series focussing on classification metrics

In the first blog, we discussed some important metrics used in regression, their pros and cons, and use cases. This part will focus on commonly used metrics in classification, why should we prefer some over others with context.

Definitions

Let’s first understand the basic terminology used in classification problems before going through the pros and cons of each method. You can skip this section if you are already familiar with the terminology.

Source of Image: Wikipedia

• Recall or Sensitivity or TPR (True Positive Rate): Number of items correctly identified as positive out of total true positives- TP/(TP+FN)
• Specificity or TNR (True Negative Rate): Number of items correctly identified as negative out of total negatives- TN/(TN+FP)
• Precision: Number of items correctly identified as positive out of total items identified…