Lab Prensentation (Chonnam National Univeristy)

1. Decision Tree
&
Random Forest Algorithm
Priagung Khusumanegara
Chonnam National Univeristy

2. Outline
 Introduction
 Example of Decision Tree
 Principles of Decision Tree
– Entropy
– Information gain
 Random Forest
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3. The problem
 Given a set of training cases/objects and their attribute
values, try to determine the target attribute value of new
examples.
– Classification
– Prediction
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4. Key Requirements
 Attribute-value description: object or case must be
expressible in terms of a fixed collection of properties or
attributes (e.g., hot, mild, cold).
 Predefined classes (target values): the target
function has discrete output values (bollean or
multiclass)
 Sufficient data: enough training cases should be
provided to learn the model.
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5. A simple example
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6. Principled Criterion
 Choosing the most useful attribute for classifying examples.
 Entropy
- A measure of homogeneity of the set of examples
- If the sample is completely homogeneous the entropy is zero and if
the sample is an equally divided it has entropy of one
 Information Gain
- Measures how well a given attribute separates the training
examples according to their target classification
- This measure is used to select among the candidate attributes at
each step while growing the tree
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7. Information Gain
Step 1 : Calculate entropy of the target
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8. Information Gain (Cont’d)
Step 2 : Calculate information gain for each
attribute
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9. Information Gain (Cont’d)
Step 3: Choose attribute with the largest
information gain as the decision node.
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10. Information Gain (Cont’d)
Step 4a: A branch with entropy of 0 is a leaf
node.
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11. Information Gain (Cont’d)
Step 4b: A branch with entropy more than 0
needs further splitting.
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12. Information Gain (Cont’d)
Step 5: The algorithm is run recursively on the
non-leaf branches, until all data is classified.
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13. Random Forest
 Decision Tree : one tree
 Random Forest : more than one tree
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14. Decision Tree & Random Forest
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Decision Tree
Random Forest
Tree 1 Tree 2
Tree 3

15. Decision Tree
Outlook Temp. Humidity Windy Play Golf
Rainy Mild High False ?
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Result : No

16. Random Forest
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Tree 1 Tree 2
Tree 3
Tree 1 : No
Tree 2 : No
Tree 3 : Yes
Yes : 1
No : 2
Result : No

17. OOB Error Rate
 OOB error rate can be used to get a running
unbiased estimate of the classification error
as trees are added to the forest.
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