This short lesson summarizes the topics we covered in section 24 and why they'll be important to you as a data scientist.
You will be able to:
- Understand and explain what was covered in this section
- Understand and explain why this section will help you become a data scientist
The key takeaways from this section include:
- An interaction is a particular property of three or more variables, where two or more variables interact in a non-additive manner when affecting a third variable
- Polynomial regression allows for better fitting data that isn't well predicted using a linear model
- The risk of polynomial regressions is that it's easier to overfit data, so it's important to consider the Bias-Variance trade-off
- Ridge and lasso regressions are two techniques for making complex models more expensive in the cost fnction, reducing the risk of overfitting
- AIC and BIC are techniques for selecting between models