the following Consider a binary classification with training data. Each data is described by 3 attributes: Color, Size, Shape. Example Color 1 Red 2 3 4 5 6 7 8 Shape Square Square Square Big Circle Big Circle Big Blue Circle Small Blue Red Size Class Big + Big + + Red Green Blue Circle Small Green Square Big + - 1. Use the entropy method to find the best attribute for the root node. (Hint: Calculate the entropy and information gain) 2. Build a complete tree according to the entropy method. The tree should include the class label and training data in each node. 3. Compute the classification error of each node of the tree.

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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the following
Consider a binary classification with
training data. Each data is described by
3 attributes: Color, Size, Shape.
Example Color
1
Red
2
3
4
5
6
7
8
Shape
Square
Blue Square Big
Red
Square
Big
Red
Circle
Big
Green
Circle
Big
Blue
Circle
Small
Blue
Circle
Small
Green Square Big
Size Class
Big +
+
+
+
1-
1. Use the entropy method to find the best attribute
for the root node. (Hint: Calculate the entropy
and information gain)
2. Build a complete tree according to the entropy
method. The tree should include the class label
and training data in each node.
3. Compute the classification error of each node of
the tree.
Transcribed Image Text:the following Consider a binary classification with training data. Each data is described by 3 attributes: Color, Size, Shape. Example Color 1 Red 2 3 4 5 6 7 8 Shape Square Blue Square Big Red Square Big Red Circle Big Green Circle Big Blue Circle Small Blue Circle Small Green Square Big Size Class Big + + + + 1- 1. Use the entropy method to find the best attribute for the root node. (Hint: Calculate the entropy and information gain) 2. Build a complete tree according to the entropy method. The tree should include the class label and training data in each node. 3. Compute the classification error of each node of the tree.
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