ot5MqUW9sBHXPQ/formResponseThis is a Decision Tree Classification Model Using Entropy Information Gain. What* ?is the depth of the decision tree in this modelX22) 02entropy0953samles26cale267. 1591entropy 0317samples 02value (267, 35)1241X122) 00eentropy0304samples 277value (262. 1511241 0678entropy0722samples 2Svalue3, 201entropy0141samples 230value 1245, 51XI21) 032entropy 0951samples 27value (17, 101XE10 0309antropy0954samplesvalue 5. 313710.171entropy w 0.34samples 79value(74. 51entropy00samples 171171. 0entropy 0323samples 17value16. 11entropy0469samples 10value 1.91entropyO1samples4value 1. 3)valu2 04.15 O14 O

The depth of a decision tree is the length of the path from a root to a leaf.

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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300-500 words per discussion and avoid plagiarism. From the question 1, given ?(?|?) =௉൫?ห?)?(?൯௣(ெ), solve the following a. Draw the probability tree for the situation. b. Draw the reverse tree for the situation. c. Discuss the False Positive from the main tree d. Discuss the True positive from the main tree e. Discuss the False negative from the main tree f. Discuss the True negative from the main tree. g. Discuss the False Positive from the main reverse tree h. Discuss the True positive from the main reverse tree i. Discuss the False negative from the main reverse tree j. Discuss the True negative from the main reverse tree.
Alice and Bob are sharing a cake. First Alice divides the cake into two pieces, then Bob chooses one piece, leaving Alice with the other piece. Alice can either divide the cake in half (a ratio of 1 : 1) or in a 1 : 2 ratio. Modeling this as a game gives the following game tree. For each of the following scenarios, write down the value for each of the three non-terminal nodes of the game treeIn other words, write down the utility that Alice should expect to receive in each of the game states given knowledge of the player strategies described below. Suppose both Alice and Bob are playing adversarially, each trying to maximize the amount of cake they receive. Suppose Alice still tries to maximize her share of the cake, but now Bob plays collaboratively, working to also help Alice get as much of the cake as possible. Suppose Alice still tries to maximize her share of the cake, but now Bob plays randomly, choosing the larger piece of cake with probability p. The state values will now be…
On this video, do you think decision tree is only use for operation research. If not, what do you think are the other uses cases of decion tree. Justify your answer 6-7 paragraph https://youtu.be/A5DbfHU7LeQ
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