Imagine we are training a decision tree, and we are at a node. Each data point is (X1, X2,X3,Y), where X1,X2, and X3 are independent variables, and Y is the dependent variable. The data are shown below. Let us train a decision tree with this data. Let's call this tree T1. What feature will we split on at the root? X1 X2 X3 Y 1 1 1 1 1 1 1 X1 X2 X1 or X2 X3

Imagine we are training a decision tree, and we are at a node. Each data point is (X1, X2,X3,Y), where X1,X2, and X3 are independent variables, and Y is the dependent variable. The data are shown below. Let us train a decision tree with this data. Let's call this tree T1. What feature will we split on at the root? X1 X2 X3 Y 1 1 1 1 1 1 1 X1 X2 X1 or X2 X3

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Map the nodes
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