A confusion matrix is a table that we use to understand the performance of a classification model. This helps us understand how we classify testing data into different classes. When we want to fine-tune our algorithms, we need to understand how the data gets misclassified before we make these changes. Some classes are worse than others, and the confusion matrix will help us understand this. Let's look at the following figure:

In the preceding chart, we can see how we categorize data into different classes. Ideally, we want all the nondiagonal elements to be 0. This would indicate perfect classification! Let's consider class 0. Overall, 52 items actually belong to class 0. We get 52 if we sum up the numbers in the first row. Now, 45 of these items are being predicted correctly, but our classifier says that four of them belong to class 1 and three of them belong to class 2. We can apply the same analysis to the remaining two rows as well. An interesting thing...