We can evaluate how well the model is working by measuring its accuracy. Accuracy would be defined as the percentage of cases that are classified correctly. We can analyze the mistakes made by the model, or its level of confusion, using a confusion matrix. The confusion matrix refers to the confusion in the model, but these confusion matrices can become a little difficult to understand when they become very large. Let's take a look at the following binary classification example, which shows the number of times that the model has made the correct predictions of the object:
In the preceding table, the rows of True apple and True orange ...
