4.1 Welcome to Unit 4
4.2 Judge, Jury, and Classifier: An Introduction to Trees
- 4.2.1 Video 1: The Supreme Court
- 4.2.2 Quick Question
- 4.2.3 Video 2: CART
- 4.2.4 Quick Question
- 4.2.5 Video 3: Splitting and Predictions
- 4.2.6 Quick Question
- 4.2.7 Video 4: CART in R
- 4.2.8 Quick Question
- 4.2.9 Video 5: Random Forests
- 4.2.10 Quick Question
- 4.2.11 Video 6: Cross-Validation
- 4.2.12 Quick Question
- 4.2.13 Video 7: The Model v. The Experts
4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story
- 4.3.1 Video 1: The Story of D2Hawkeye
- 4.3.2 Quick Question
- 4.3.3 Video 2: Claims Data
- 4.3.4 Quick Question
- 4.3.5 Video 3: The Variables
- 4.3.6 Quick Question
- 4.3.7 Video 4: Error Measures
- 4.3.8 Quick Question
- 4.3.9 Video 5: CART to Predict Cost
- 4.3.10 Quick Question
- 4.3.11 Video 6: Claims Data in R
- 4.3.12 Quick Question
- 4.3.13 Video 7: Baseline Method and Penalty Matrix
- 4.3.14 Quick Question
- 4.3.15 Video 8: Predicting Healthcare Cost in R
- 4.3.16 Quick Question
- 4.3.17 Video 9: Results
4.4 Location, Location, Location: Regression Trees for Housing Data (Recitation)
- 4.4.1 Welcome to Recitation 4
- 4.4.2 Video 1: Boston Housing Data
- 4.4.3 Video 2: The Data
- 4.4.4 Video 3: Geographical Predictions
- 4.4.5 Video 4: Regression Trees
- 4.4.6 Video 5: Putting it all Together
- 4.4.7 Video 6: The CP Parameter
- 4.4.8 Video 7: Cross-Validation
4.5 Assignment 4
- 4.5.1 Predicting Earnings from Census Data
- 4.5.2 Understanding Why People Vote
- 4.5.3 State Data Revisted
Back: 3.5 Assignment 3 Predicting the Baseball World Series Champion
