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# 203.3.5 Information Gain in Decision Tree Split

## Information Gain

In previous section, we studied about How to Calculate Entropy for Decision Tree Split?

• Information Gain= entropyBeforeSplit – entropyAfterSplit
• Easy way to understand Information gain= (overall entropy at parent node) – (sum of weighted entropy at each child node)
• Attribute with maximum information is best split attribute

### Information Gain- Calculation • Entropy Ovearll = 100% (Impurity)
• Entropy Young Segment = 99%
• Entropy Old Sgment = 99%
• Information Gain for Age =100-(0.699+0.499)=1 • Entropy Ovearll = 100% (Impurity)
• Entropy Male Segment = 72%
• Entropy Female Sgment = 29%
• Information Gain for Age =100-(0.672+0.429)=45.2

### LAB: Information Gain

Calculate the information gain this example base on the variable split  ### Output-Information Gain

Split With Respect to ‘Owning a car’

• Entropy([28+,39-]) Ovearll = -28/67 log2 28/67 – 39/67 log2 39/67 = 98% (Impurity)
• Entropy([25+,4-]) Owing a car = 57%
• Entropy([3+,35-]) No car = 40%
• Information Gain for Owing a car =98-((29/67)57+(38/67)40)=50.6

Split With Respect to ‘Gender’

• Entropy([19+,21-]) Male= 99%
• Entropy([9+,18-]) Female = 91%
• Information Gain for Gender=98-((40/67)99+(27/67)91) =2.2

## Other Purity (Diversity) Measures

• Chi-square measure of association
• Gini Index : Gini(T) = $$1 – \sum p_j^2$$
• Information Gain Ratio
• Misclassification error

The next post is about The Decision Tree Algorithm.

20th June 2017
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