Simply calculates the splits based on the amazing Hoeffding bound. This is just for research purposes.
Reference paper:
@inproceedings{domingos2000mining,
title={Mining high-speed data streams},
author={Domingos, Pedro and Hulten, Geoff},
booktitle={Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={71--80},
year={2000}
}
Sample data:
| Person | Time since license | Gender | Area | Risk class |
|---|---|---|---|---|
| 1 | 1 − 2 | m | urban | low |
| 2 | 2 − 7 | m | rural | high |
| 3 | > 7 | f | rural | low |
| 4 | 1 − 2 | f | rural | high |
| 5 | > 7 | m | rural | high |
| 6 | 1 − 2 | m | rural | high |
| 7 | 2 − 7 | f | urban | low |
| 8 | 2 − 7 | m | urban | low |
Sample output (with verbose set to True):
2
dict_keys(['1-2', '2-7'])
entropy for 1-2 : -0.0
entropy for 2-7 : -0.0
weighted_sum: 0.0
infogain: 1.0 - 0.0 = 1.0
dict_keys(['m'])
entropy for m : 1.0
weighted_sum: 1.0
infogain: 1.0 - 1.0 = 0.0
dict_keys(['urban', 'rural'])
entropy for urban : -0.0
entropy for rural : -0.0
weighted_sum: 0.0
infogain: 1.0 - 0.0 = 1.0
All infogains: [(0.0, 'gender'), (1.0, 'area'), (1.0, 'time')]
4
dict_keys(['1-2', '2-7', '>7'])
entropy for 1-2 : 1.0
entropy for 2-7 : -0.0
entropy for >7 : -0.0
weighted_sum: 0.5
infogain: 1.0 - 0.5 = 0.5
dict_keys(['m', 'f'])
entropy for m : 1.0
entropy for f : 1.0
weighted_sum: 1.0
infogain: 1.0 - 1.0 = 0.0
dict_keys(['urban', 'rural'])
entropy for urban : -0.0
entropy for rural : 0.9182958340544896
weighted_sum: 0.6887218755408672
infogain: 1.0 - 0.6887218755408672 = 0.31127812445913283
All infogains: [(0.0, 'gender'), (0.31127812445913283, 'area'), (0.5, 'time')]
6
dict_keys(['1-2', '2-7', '>7'])
entropy for 1-2 : 0.9182958340544896
entropy for 2-7 : -0.0
entropy for >7 : 1.0
weighted_sum: 0.792481250360578
infogain: 0.9182958340544896 - 0.792481250360578 = 0.12581458369391152
dict_keys(['m', 'f'])
entropy for m : 0.8112781244591328
entropy for f : 1.0
weighted_sum: 0.8741854163060885
infogain: 0.9182958340544896 - 0.8741854163060885 = 0.044110417748401076
dict_keys(['urban', 'rural'])
entropy for urban : -0.0
entropy for rural : 0.7219280948873623
weighted_sum: 0.6016067457394686
infogain: 0.9182958340544896 - 0.6016067457394686 = 0.31668908831502096
All infogains: [(0.044110417748401076, 'gender'), (0.12581458369391152, 'time'), (0.31668908831502096, 'area')]
8
dict_keys(['1-2', '2-7', '>7'])
entropy for 1-2 : 0.9182958340544896
entropy for 2-7 : 0.9182958340544896
entropy for >7 : 1.0
weighted_sum: 0.9387218755408672
infogain: 1.0 - 0.9387218755408672 = 0.06127812445913283
dict_keys(['m', 'f'])
entropy for m : 0.9709505944546686
entropy for f : 0.9182958340544896
weighted_sum: 0.9512050593046015
infogain: 1.0 - 0.9512050593046015 = 0.04879494069539847
dict_keys(['urban', 'rural'])
entropy for urban : -0.0
entropy for rural : 0.7219280948873623
weighted_sum: 0.4512050593046014
infogain: 1.0 - 0.4512050593046014 = 0.5487949406953986
All infogains: [(0.04879494069539847, 'gender'), (0.06127812445913283, 'time'), (0.5487949406953986, 'area')]
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