Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) ID Marital Refund Тахable Cheat Status Income Single Married 1 Yes 125K No 2 No 100K No Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes 6. Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes 9. Married No 75K No 10 Single No 90K Yes 222

Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) ID Marital Refund Тахable Cheat Status Income Single Married 1 Yes 125K No 2 No 100K No Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes 6. Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes 9. Married No 75K No 10 Single No 90K Yes 222

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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In the traning dataset given below, a) Extract the decision tree for the above problem. b) Guess the value that will come to the place where "?" is?
Assume we have three binary input attributes, A, B, C, and one binary outcome attribute Y. The three input attributes, A, B, C take values in the set {0,1} while the Y attribute takes values in the set {True, False} a. Which attribute (A, B, or C) has the highest information gain? b. Construct the full decision tree for this problem, without doing any pruning. If at any point you have a choice between splitting on two equally desirable attributes, choose the one that comes first alphabetically. If there is a tie on how to label a leaf, then choose True.(NEED ONLY HANDWRITTEN SOLUTIION)
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Method: Python Dataset: Census Income Source: https://archive.ics.uci.edu/ml/datasets/Adult Objective The objective of this task is to implement from scratch Decision Tree classification method to predict whether the incomes exceed $50K/yr based on census data. Thus, this is a binary classification problem. The training and test sets are pre-defined in the data set (i.e., in "adult.data" and "adult.test"). Requirements (1) Implement two DT models by choosing any two (2) split criteria from Information Gain, Gain Ratio, Gini Index and Variance. Note that you can use either binary-split or multiple-split. (2) Use (approximately) 2/3 records in "adult.data" for training, and 1/3 records in "adult.data" for post-pruning. (3) Report the accuracy of each model. (4) All DT models must be self-implemented. You CANNOT use any machine learning library in this task. (5) It is recommended that your implementation includes a "tree induction function", a "classification function" and a…
For the following politically incorrect data from Quinlan: Height Hair Eyes Attractive? Small Blonde Brown No Tall Dark Brown No Tall Blonde Blue Yes Tall Dark Blue No Small Dark Blue No Tall Red Blue Yes Tall Blonde Brown No Small Blonde Blue Yes Turn this data into a decision tree to classify which attributes make a person attractive, and then extract the rules. No programming use paper and pencil. Using Scikit-Learn train a decision tree to predict whether a person with a small height, dark hair, and brown eyes is an attractive person or not
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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.