Performance Improvement Tactics

Performance improvement for supervised machine learning models is an iterative process, and a continuous cycle of updating and evaluation is usually required to get the perfect model. While the previous sections in this chapter dealt with the evaluation strategies, this section will talk about model updating: we will discuss some ways we can determine what our model needs to give it that performance boost, and how to make that change in our model.

Variation in Train and Test Error

In the previous chapter, we introduced the concepts of underfitting and overfitting, and mentioned a few ways to overcome them, later introducing ensemble models. But we didn't talk about how to identify whether our model was underfitting or overfitting to the training data.

It's usually useful to look at the learning and validation curves.

Learning Curve

The learning curve shows the variation in the training and validation error with the training data increasing in size. By looking at...