Summary

In this chapter, we discussed the concept of MLR and topics aiding in its implementation. These topics included feature selection methods, shrinkage methods, and PCR. Using these tools, we were able to demonstrate approaches to reduce the risk of modeling excess variance. In doing so, we were able to also induce model bias so that models can have a better chance of generalizing on unseen data with minimal complications as frequently faced when overfitting.

In the next chapter, we will begin a discussion on classification with the introduction of logistic regression, which fits a sigmoid to a linear regression model to derive probabilities of binary class membership.