Important evaluation metrics – classification algorithms
Most of the metrics used to assess a classification model are based on the values that we get in the four quadrants of a confusion matrix. Let's begin this section by understanding what it is:
- Confusion matrix: It is the cornerstone of evaluating a classification model (that is, classifier). As the name stands, the matrix is sometimes confusing. Let's try to visualize the confusion matrix as two axes in a graph. The xaxis label is prediction, with two values—Positive and Negative. Similarly, theyaxis label is actually with the same two values—PositiveandNegative, as shown in the following figure. This matrix is a table that contains the information about the count of actual and predicted values by a classifier:
- If we try to deduce information about each quadrant in the matrix:
- Quadrant one is the number of positive class predictions that were accurately identified. So, it is termed as True Positive (TP).
- Quadrant two, also known as False...
