Once we have trained a model, to evaluate, it is highly necessary to check its overall validity. In a binary classification problem, setting the evaluation is done by using the following output values. Here, we want to evaluate the model's performance for category A:
- True positive (TP): Given a sample from label A, the output is also categorized as A
- True negative (TN): Given a sample from label A, the output is categorized into B
- False positive (FP): Given a sample from label B, the output is categorized into A
- False negative (FN): Given a sample from B, the output is also categorized into B
This is done for the evaluation set, and based on it, we compute the following parameters.
