3Consider the decision trees shown in Figure 1. The decision tree in 1b is a pruned version of the originaldecision tree 1a. The training and test sets are shown in table 5. For every combination of values forattributes A and B, we have the number of instances in our dataset that have a positive or negative label.03.1+03.4B0001101 1~~3.52 02 110+5322726A(a) Decision Tree 1 (DT1)216BTraining set# of (+) instances #of (-)1034347325425instances4B1+Figure 1Table 5+403635203+A1BTest set# of (+) instances # of (-) instances3.3Build the confusion matrices when using DT1 and DT2 to predict for the test set.1(b) Decision Tree 2 (DT2)Estimate the generalization error rate of both trees shown in Figure 1 (DT1, DT2) using the optimisticapproach and the pessimistic approach. To account for model complexity with the pessimistic approach,use a penalty value of 2 = 2 for each leaf node.Compute the error rate of the DT1 and DT2 on the test set shown in the table 5.3.2Which tree would you select? In other words, would you use the original decision tree or would you pruneit? Briefly explain how you will decide.21131525Calculate the test accuracy, precision, recall, and F1-score of DT1 and DT2 for the (+) class.

Consider the decision trees shown in Figure 1. The decision tree in 1b is a pruned version of the original decision tree 1a. The training and test sets are shown in table 5. For every combination of values for attributes A and B, we have the number of instances in our dataset that have a positive or negative label.

Consider the decision trees shown in Figure 1. The decision tree in 1b is a pruned version of the original decision tree 1a. The training and test sets are shown in table 5. For every combination of values for attributes A and B, we have the number of instances in our dataset that have a positive or negative label.

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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Draw the decision tree for training sample set in the picture with the CART (Classification and Regression Tree) algorithm by making the necessary calculations. Thank you so much.
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