Week 7 - Classification and model building
Logistic regression for predicting categorical data and model building 🚧
Painted wall. Photo by Deepak Verma on Unsplash
Tasks
- Review the slides
- Complete the assignments
- Complete the readings
Topics
No. |
Title |
Slides |
|
|---|---|---|---|
| 1 | Logistic regression | ||
| 2 | Prediction and overfitting |
Class Activities
Activity |
Title |
Date |
|---|---|---|
| Topic Intro | Logistic Regression | Tue, 20 Feb |
| Topic Intro | Prediction and overfitting | Wed, 21 Feb |
| Lab 05 | Lab 3 GSS | Fri, 23 Feb |
Assignments
Assignment |
Title |
Due |
|---|---|---|
| Due this week | ||
| Lab 05 | Grade the Professor Part 2 | Thu, 22 Feb 23:59 EST |
| HW 03 Optional | Bike rentals DC | Fri, 23 Feb 23:59 EST |
| Q 03 | Multiple regression | Sun, 25 Feb, 23:59 EST |
| Due next week | ||
| Lab 06 | GSS Survey | Fri, 1 Mar 23:59 EST |
| Q 04 | Modeling overview | Sun, 3 Mar, 23:59 EST |
Readings
| 📖 | IMS: Chp 9 - Logistic regression | Required |
| 📄 | tidymodels: Build a model | Required |
| 📄 | MLU-Explain: ROC Curves Explained | Optional |
| 📄 | MLU-Explain: Logistic Regression | Optional |
| 📄 | MLU-Explain: Train and Test and Validation | Optional |
Interactive R tutorials
The following are interactive R tutorials, designed to give you more practice with R. These are optional, but they will show up in your next homework assignment, so you should gain familiarity with it. If you’re struggling with any of the topics covered this week, I strongly recommend you work through the interactive tutorials.
| Exploring the GSS | Related to Lab 05 |
