This is a Decision Tree Classification Model Using Entropy Information Gain. What * ?is the depth of the decision tree in this model X22) 02 entropy 53 samples 426 alue267. 159 27 302 entropy0317 samples 302 calue (267, 25) 124 X22) 00 ntropy0304 samples 277 value(262. 15) 241 0678 entropy 0722 samples 25 value-13, 201 entropy0141 samples 250 value 245. 51 X21) 032 entropy 0951 samples 27 value (17, 101 TOP0-Stb entropy 54 samples value3.31 entropy 0.34 samples 79 value (74 5 opy-00 171 071, 01 entropy0.32 ples-17 n6 11 entropy0.49 samples 10 value 1.9 entropy 01 samples 4 value . 31 eneropy w 0 D samples 4 14.01
This is a Decision Tree Classification Model Using Entropy Information Gain. What * ?is the depth of the decision tree in this model X22) 02 entropy 53 samples 426 alue267. 159 27 302 entropy0317 samples 302 calue (267, 25) 124 X22) 00 ntropy0304 samples 277 value(262. 15) 241 0678 entropy 0722 samples 25 value-13, 201 entropy0141 samples 250 value 245. 51 X21) 032 entropy 0951 samples 27 value (17, 101 TOP0-Stb entropy 54 samples value3.31 entropy 0.34 samples 79 value (74 5 opy-00 171 071, 01 entropy0.32 ples-17 n6 11 entropy0.49 samples 10 value 1.9 entropy 01 samples 4 value . 31 eneropy w 0 D samples 4 14.01
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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