Week 10 - 10/23

Date: Oct 23th - Oct 25th

What we will cover

In this class, we wil switch gears and start with our prediction chapter. We’ll be talking about model checking, bias-variance tradeoff, cross-validation, and variable selection.

  • James, G. et al. (2021). “An Introduction to Statistical Learning with Applications in R” (ISLR). Chapter 2.1.3 (pg. 24-26), Chapter 2.2: 2.2.1 and 2.2.2 (pg. 29-36), and Chapter 6: 6.1.2 (pg. 225-236 and 229-232). Note: For ISLR readings, don’t get caught up in the math.

  • StatQuest. (2018). “Machine Learning Fundamentals: Bias and Variance”.

  • Ritvik Kharkar. (2020). “Cross-Validation: Data Science Concepts”. Video materials from ritvikmath.
  • OpenIntroOrg. (2015). “Model Selection in Multiple Regression”. Video materials from OpenIntro.


New window Download


Here is the R code we will review in class, with many additional questions! Remember to review it in detail after class Download

Check out the in-class activity we did for this week Download

(The answers for this are here: Download)

Additional Materials

To better understand the importance of the variance-bias concept, I find this video very insightful (watch it after we cover these concepts in class if you need better understanding of this topic!)

  • Ritvik Kharkar. (2020). “Bias-Variance tradeoff: Data Science Basics”. Video materials from ritvikmath.

© Magdalena Bennett - licensed under Creative Commons.