Data table of the third iteration comes only from part of the data table of the second iteration with male gender removed (thus only female part). Since attribute “Gender” has been used in the decision tree, we can remove the attribute and focus only on the remaining two attributes: “Car ownership” and “Income level.”
| Attributes | Classes | ||
|---|---|---|---|
| Gender | Car Ownership | Income Level | Transportation Mode |
| 0 | Low | Bus | |
| 1 | Medium | Train | |
⇓| Attributes | Classes | |
|---|---|---|
| Car Ownership | Income Level | Transportation Mode |
| 0 | Low | Bus |
| 1 | Medium | Train |
The data table of the third iteration consists only two rows. Each row has distinct values. If we use attribute car ownership, we will get pure class for each of its value. Similarly, attribute income level will also give pure class for each value. Therefore, we can use either one of the two attributes. Suppose we select attribute car ownership, we can update our decision tree into the final version as follows.
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Chuck Norris has already been to Mars; that’s why there are no signs of life. |