Week 9

Date: Oct 26th - Oct 28th

What we will cover

This week we talk about binary outcome models, and will start diving into into prediction and model selection problems. We’ll be talking about model cheking, cross-validation, bias-variance tradeoff, 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.

  • OpenIntroOrg. (2015). “Model Selection in Multiple Regression”. Video materials from OpenIntro.

  • Ritvik Kharkar. (2020). “Cross Validation : Data Science Concepts”. Video materials from ritvikmath.

JITT

Complete before Sunday Oct 24th (11:59 pm) (if your section is on Tue) or Tuesday Oct 26th (11:59 pm) (if your section is on Thu). You can find the assignment here.

Slides

New window Download


New window Download

Code

Code for Binary Outcomes Download


Code for Model Selection I Download

Resources

Here is a video where I go in more detail into the prediction code for this week. Check it out if you have any questions!






© Magdalena Bennett - licensed under Creative Commons.