DecisionTreeID3
DecisionTreeID3 copied to clipboard
Published
20 hours ago •
tiepvupsu
tiepvupsu
My implementation of Decision Tree ID3 algorithm for all categorical attributes.
DecisionTreeID3
Implement ID3 algorithm for data with all categorical attributes by using panda and numpy (sklearn DecisionTreeClassifier doesn't support categorical attributes).
Stopping criteria
-
max_depth: the max depth of the tree. -
min_samples_split: the minimum number of samples in a split to be considered. -
min_gain: minimum gain for splitting.
simple test on weather.csv
| id | outlook | temperature | humidity | wind | play |
|---|---|---|---|---|---|
| 1 | sunny | hot | high | weak | no |
| 2 | sunny | hot | high | strong | no |
| 3 | overcast | hot | high | weak | yes |
| 4 | rainy | mild | high | weak | yes |
| 5 | rainy | cool | normal | weak | yes |
| 6 | rainy | cool | normal | strong | no |
| 7 | overcast | cool | normal | strong | yes |
| 8 | sunny | mild | high | weak | no |
| 9 | sunny | cool | normal | weak | yes |
| 10 | rainy | mild | normal | weak | yes |
| 11 | sunny | mild | normal | strong | yes |
| 12 | overcast | mild | high | strong | yes |
| 13 | overcast | hot | normal | weak | yes |
| 14 | rainy | mild | high | strong | no |
python id3.py
Result should be:
['no', 'no', 'yes', 'yes', 'yes', 'no', 'yes', 'no', 'yes', 'yes', 'yes', 'yes', 'yes', 'no']
It means 100% accuracy on training set.
Pruning might be added later.
tiepvupsu