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Machine Learning Decision Trees. Exercise Solutions Exercise 1 a) Machine learning methods are often categorised in three main types: supervised, unsupervised and ... – PowerPoint PPT presentation

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Title: Machine%20Learning


1
Machine Learning
  • Decision Trees.
  • Exercise Solutions

2
Exercise 1
  • a) Machine learning methods are often categorised
    in three main types supervised, unsupervised and
    reinforcement learning methods. Explain these in
    not more than a sentence each and explain in
    which category does Decision Tree Learning fall
    and why?

3
Answer
  • Supervised learning is learning with a teacher,
    i.e. input-output examples are given to the
    system in the training phase. After training the
    system is asked to predict the output from new
    inputs. E.g. classification
  • Unsupervised learning is in fact learning for
    structure discovery with no teacher. Only input
    data are seen in both the training and the
    testing phase. E.g. ICA, clustering.
  • Reinforcement learning is learning with no
    teacher but with feedback from the environment.
    The feedback consists of rewards, which are
    typically delayed. E.g. Q-learning.
  • ?Decision Trees are supervised learning
    methods.They do classification based on given
    examples.

4
  • c) For the sunbathers example given in the
    lecture, calculate the Disorder function for the
    attribute height at the root node.

5
Disorder of height

6
Disorder of height (contd)
Alex Annie Katie
Sarah Emily John
7
Exercise 2
  • For the sunbathers example given in the lecture,
    calculate the Disorder function associated with
    the possible branches of the decision tree once
    the root node (hair colour) has been chosen.

8
Answer 1st branch
is_sunburned
Hair colour
Sarah AnnieDana Katie
Blonde
Height
Weight
Lotion used
Short
Tall
Average
Average
Light
Yes
No
Sarah Annie
Dana Katie
Sarah
Annie Katie
Dana
Sarah Katie
AnnieDana
0.5
0
1.0
9
  • So in this branch (1st branch) we found the
    Lotion Used is the next attribute to split on
  • We also found that by doing that this branch is
    done.
  • The method of computation for the other 2
    branches (red and brown) is exactly the same.

10
Exercise 3
  • Using the decision tree learning algorithm,
    calculate the decision tree for the following
    data set

11
Data for Exercise 3
12
Ex 3 Search for Root. Candidate Hair Colour
is_sunburned
Hair colour
Brown
Blonde
Sarah AnnieDana Julie Ruth
Alex Pete John
Av Disorder (5/8) 0.971 0.6069
13
Ex 3 Search for Root. Candidate Height
is_sunburned
Height
Short
Tall
Average
Alex Annie
Sarah Julie John Ruth
Dana Pete
Av Disorder ¼ 1/2 0.8113 0 0.655
14
Ex 3 Search for Root. Candidate Weight
is_sunburned
Weight
Light
Heavy
Average
Sarah Julie Ruth
Pete John
Dana Alex Annie
Av Disorder 2(3/8)0.9183 0.6887
15
Ex 3 Search for Root. Candidate Lotion
is_sunburned
Lotion used
Sarah Annie Julie Pete John Ruth
No
Yes
Dana Alex
Av Disorder (3/4)0.9183 0.6887
16
Ex 3 Next
Dana
17
Ex 3 Next
is_sunburned
Hair colour
Blonde
Brown
Height
No
Short
Tall
Av
No
Yes
Sarah Julie Ruth
No further split will improve the classification
accuracy on the training data. We can assign a
decision to this leaf node based on the majority.
That gives a No.
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