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.