Class Activity: Decision Trees Objective: To solve a classification problem using a decision tree algorithm. Sample Dataset: Customer Turnover Customer Age Salary Tenure Turnover (Yes/No) 22 25000 2 No 35 40000 5 No 30 60000 10 No 45 50000 3 Yes 25 30000 1 Yes 50 70000 8 No 23 32000 1 Yes 40 45000 No 33 38000 6 Yes 29 52000 7 No 55 80000 38 48000 31 54000 42 62000 2913 No Yes 11 No Yes Tasks: 。 Identify the root node by calculating the information gain for each feature (Customer Age, Salary, Tenure). 。 Create the first split in the dataset. 。 Continue splitting the data to construct the full decision tree until all instances are classified.
Class Activity: Decision Trees Objective: To solve a classification problem using a decision tree algorithm. Sample Dataset: Customer Turnover Customer Age Salary Tenure Turnover (Yes/No) 22 25000 2 No 35 40000 5 No 30 60000 10 No 45 50000 3 Yes 25 30000 1 Yes 50 70000 8 No 23 32000 1 Yes 40 45000 No 33 38000 6 Yes 29 52000 7 No 55 80000 38 48000 31 54000 42 62000 2913 No Yes 11 No Yes Tasks: 。 Identify the root node by calculating the information gain for each feature (Customer Age, Salary, Tenure). 。 Create the first split in the dataset. 。 Continue splitting the data to construct the full decision tree until all instances are classified.
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Pls solve this Question By hand use the ID3 Algorithm.
Transcribed Image Text:Class Activity: Decision Trees
Objective:
To solve a classification problem using a decision tree algorithm.
Sample Dataset: Customer Turnover
Customer Age
Salary
Tenure
Turnover (Yes/No)
22
25000
2
No
35
40000
5
No
30
60000
10
No
45
50000
3
Yes
25
30000
1
Yes
50
70000
8
No
23
32000
1
Yes
40
45000
No
33
38000
6
Yes
29
52000
7
No
55
80000
38
48000
31
54000
42
62000
2913
No
Yes
11
No
Yes
Tasks:
。 Identify the root node by calculating the information gain for each feature (Customer Age,
Salary, Tenure).
。 Create the first split in the dataset.
。 Continue splitting the data to construct the full decision tree until all instances are classified.
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