代写COMP5318/COMP4318 Machine Learning and Data Mining Week 5代做Java程序
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s1 2025
Week 5 Tutorial exercises
Decision Trees
Exercise 1. Decision trees and information gain (parts a) and b) - done in class; the rest inyour own time)
Consider the following set of training examples:
|
shape |
color |
class |
|
circle |
blue |
+ |
|
circle |
blue |
+ |
|
square |
blue |
- |
|
triangle |
blue |
- |
|
square |
red |
+ |
|
square |
blue |
- |
|
square |
red |
+ |
|
circle |
red |
+ |
Adapted from M. Kubat, Introduction to Machine Learning, Springer, 2021
a) What is the entropy of this collection of training examples with respect to the class?
b) What is the information gain of the attribute shape?
c) Which attribute will be selected as root of the tree based on information gain?
d) Build the whole decision tree. Draw the tree after each selected attribute.
You may use this table to calculate information gain:
|
x |
y |
-(x/y)* log2(x/y) |
x |
y |
-(x/y)* log2(x/y |
x |
y |
-(x/y)* log2(x/y |
x |
y |
-(x/y)* log2(x/y |
|
1 |
2 |
0.50 |
4 |
5 |
0.26 |
6 |
7 |
0.19 |
5 |
9 |
0.47 |
|
1 |
3 |
0.53 |
1 |
6 |
0.43 |
1 |
8 |
0.38 |
7 |
9 |
0.28 |
|
2 |
3 |
0.39 |
5 |
6 |
0.22 |
3 |
8 |
0.53 |
8 |
9 |
0.15 |
|
1 |
4 |
0.50 |
1 |
7 |
0.40 |
5 |
8 |
0.42 |
1 |
10 |
0.33 |
|
3 |
4 |
0.31 |
2 |
7 |
0.52 |
7 |
8 |
0.17 |
3 |
10 |
0.52 |
|
1 |
5 |
0.46 |
3 |
7 |
0.52 |
1 |
9 |
0.35 |
7 |
10 |
0.36 |
|
2 |
5 |
0.53 |
4 |
7 |
0.46 |
2 |
9 |
0.48 |
9 |
10 |
0.14 |
|
3 |
5 |
0.44 |
5 |
7 |
0.35 |
4 |
9 |
0.52 |
|
|
|