It is key for you to understand why we need to evaluate the performance of a model in the first place. Some of the potential reasons why performance evaluation is critical are as follows:
- It prevents overfitting: Overfitting occurs when your algorithm hugs the data too tightly and makes predictions that are specific to only one dataset. In other words, your model cannot generalize its predictions outside of the data that it was trained on.
- It prevents underfitting: This is the exact opposite of overfitting. In this case, the model is very generic in nature.
- Understanding predictions: Performance evaluation methods will help you to understand, in greater detail, how your model makes predictions, along with the nature of those predictions and other useful information, such as the accuracy of your model.