| The illustration shows the computation of information gain for the first iteration (based on the data table) for other three attributes of “Gender,” “Car ownership,” and “Income level.” |
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Table below summarizes the information gain for all four attributes. In practice, you don’t need to compute the impurity degree based on three methods. You can use any one of entropy, Gini index, and classification error.
| Results of the First Iteration for Information Gain Computation | ||||
|---|---|---|---|---|
| Gain | Gender | Car Ownership | Travel Cost/km | Income Level |
| Entropy | 0.125 | 0.534 | 1.210 | 0.695 |
| Gini index | 0.060 | 0.207 | 0.500 | 0.293 |
| Classification error | 0.100 | 0.200 | 0.500 | 0.300 |
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