4.2 Judge, Jury, and Classifier: An Introduction to Trees | 4.2 Judge, Jury, and Classifier: An Introduction to Trees | 4 Trees | The Analytics Edge | Sloan School of Management

<Video 2: CART
4.2.1Video 1: The Supreme Court
4.2.2Quick Question
4.2.3Video 2: CART
4.2.4Quick Question
4.2.5Video 3: Splitting and Predictions
4.2.6Quick Question
4.2.7Video 4: CART in R
4.2.8Quick Question
4.2.9Video 5: Random Forests
4.2.10Quick Question
4.2.11Video 6: Cross-Validation
4.2.12Quick Question
4.2.13Video 7: The Model v. The Experts
>Video 3: Splitting and Predictions

Quick Question

Suppose that you have the following CART tree:

How many splits are in this tree?

For which data observations should we predict "Red", according to this tree? Select all that apply.

If X is less than 60, and Y is any value.

If X is greater than or equal to 60, and Y is greater than or equal to 20.

If X is greater than or equal to 85, and Y is less than 20.

If X is greater than or equal to 60 and less than 85, and Y is less than 20.

Explanation

This tree has three splits. The first split says to predict "Red" if X is less than 60, regardless of the value of Y. Otherwise, we move to the second split. The second split says to check the value of Y - if it is greater than or equal to 20, predict "Gray". Otherwise, we move to the third split. This split checks the value of X again. If X is less than 85 (and greater than or equal to 60 by the first split) and Y is less than 20, then we predict "Red". Otherwise, we predict "Gray".