I did not understand how we use features that were one hot encoded in decision tree. How to do they split at each node with calculating information gain.
Hi @Aman6
Original Dataset:
| Numeric | Category |
|---|---|
| 5 | Red |
| 8 | Blue |
| 3 | Green |
| 6 | Yellow |
| After One-Hot Encoding and Removing the “Category” Column: |
| Numeric | Red | Blue | Green | Yellow |
|---|---|---|---|---|
| 5 | 1 | 0 | 0 | 0 |
| 8 | 0 | 1 | 0 | 0 |
| 3 | 0 | 0 | 1 | 0 |
| 6 | 0 | 0 | 0 | 1 |
after we convet the data we deal with it like any another dataset
