3 Nonparametric Regression
- Start: Monday, February 8
- End: Friday, February 12
3.1 Summary
This week we will continue discussing the regression task. We will introduce to new methods, k-nearest neighbors and decisions trees, which will serve as examples of nonparametric modeling techniques. We will also discuss model flexibility and how it relates to the overfitting and bias-variance tradeoff.
- Keywords: Nonparametric Regression, k-Nearest Neighbors, Decision Trees, Model Flexibility, Tuning Parameters, Bias-Variance Tradeoff, Overfitting, No Free Lunch, Curse of Dimensionality
3.2 Learning Objectives
After completing this week, you are expected to be able to:
- Differentiate between parametric and nonparametric regression.
- Understand how model flexibility relates to the bias-variance tradeoff and thus model performance.
- Use R packages and functions to fit KNN and decision tree models and make predictions or estimate conditional means.
- Select models by manipulating their flexibility through the use of a tuning parameter.
- Avoid overfitting by selecting an a model of appropriate flexibility through the use of a validation set.
3.4 Video
Title | Link | Mirror |
---|---|---|
3.1 - Welcome to Week 03 | 3.1 - YouTube | 3.1 - ClassTranscribe |
3.2 - Nonparametric Regression | 3.2 - YouTube | 3.2 - ClassTranscribe |
3.3 - Nonparametric Regression in R | 3.3 - YouTube | 3.3 - ClassTranscribe |
3.4 - Supervised Learning Concepts | 3.4 - YouTube | 3.4 - ClassTranscribe |
3.6 Office Hours
Staff and Link | Day | Time |
---|---|---|
Zoom with David | Monday | 8:00 PM - 9:00 PM |
Zoom with Tianyi | Monday | 9:00 PM - 10:00 PM |
Zoom with David | Thursday | 8:00 PM - 9:00 PM |
Zoom with Tianyi | Thursday | 9:00 PM - 10:00 PM |
Piazza | Any! | Any! |