Introducing model evaluation

There are several different scenarios in which you might want to evaluate model performance. Some of them are as follows:

• You are creating a model and testing different approaches and/or algorithms. Therefore, you need to compare these models to select the best one.
• You have just completed your model and you need to document your work, which includes specifying the model’s performance metrics that you got from the modeling phase.
• Your model is running in a production environment, and you need to track its performance. If you encounter model drift, then you might want to retrain the model.

Important note

The term model drift is used to refer to the problem of model deterioration. When you are building a machine learning model, you must use data to train the algorithm. This set of data is known as training data, and it reflects the business rules at a particular point in time. If these business rules change over time, your...