Confusion Matrix

A confusion matrix helps to illustrate what kinds of errors a classification model is making.

If you have a binary classifier model that distinguishes between a positive and a negative class, you can define the following 4 values depending on the actual vs predicted class.

Confusion matrix

The resulting matrix has 4 fields known as:

• True Positives (TP)
• True Negatives (TN)
• False Positives (FP),
• False Negatives (FN)

Different combinations of these fields result in a number of key metrics, including: accuracy, precision, recall, specificity and f1 score.

The confusion matrix is a compact but very informative representation of how your classification model is performing. It is the go to tool for evaluating classification models.