This is a table that describes the classification model's performance. It is an n rows, n columns matrix where n represents the number of classes that are predicted by the classification model. It is formed by noting down the number of correct and incorrect predictions by the model when compared to the actual label.

Confusion matrices are better explained with an example—assume that there are 100 images in a dataset where there are 50 dog images and 50 cat images. A model that is built to classify images as cat images or dog images is given this dataset. The output from the model showed that 40 dog images are classified correctly and 20 cat images are predicted correctly. The following table is the confusion matrix construction from the prediction output of the model: