Extension to Linear Models - Recap

Introduction

Congratulations, you've just modeled some complex non-linear relationships with interaction terms and polynomials! Here is a recap of what you learned in this section.

Key Takeaways

This section gave you the chance to learn about techniques whereby you can model non-linear relationships between the predictor and target variables. It's very rare that real-world problems can be modeled with a simple linear regression, so it's important to get yourself well acquainted with creating new features and selecting the most important ones.

• An interaction is a particular property of two or more variables where they interact in a non-additive manner when affecting a third variable
• Polynomial regression allows for bringing in higher orders of predictor variables (such as squared, cubed, etc)
• The risk of polynomial regression is that they can easily overfit to data, so it's important to consider the Bias-variance tradeoff when building models with greater complexity

Summary

Excellent work! You learned a substantial amount about different ways to model non-linear relationships. You will continue to use and build upon the concepts learned in this section for the rest of your machine learning career.