40 Algorithms Every Programmer Should Know

Evaluating the classifiers

Once the model is trained, we need to evaluate its performance. To do that, we will use the following process:

1. We will divide the labeling dataset into two parts—a training partition and a testing partition. We will use the testing partition to evaluate the trained model.

2. We will use the features of our testing partition to generate labels for each row. This is our set of predicted labels.

3. We will compare the set of predicted labels with the actual labels to evaluate the model.

Unless we are trying to solve something quite trivial, there will be some misclassifications when we evaluate the model. How we interpret these misclassifications to determine the quality of the model depends on which performance metrics ...