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Update comments and README.md

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Ricardo Montañana Gómez
2021-11-02 14:04:10 +01:00
parent fc48bc8ba4
commit b044a057df
3 changed files with 9 additions and 3 deletions
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README.md
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@@ -73,3 +73,7 @@ python -m unittest -v stree.tests
## License
STree is [MIT](https://github.com/doctorado-ml/stree/blob/master/LICENSE) licensed
## Reference
R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class oblique decision tree based on support vector machines.", 2021 LNAI 12882, pg. 54-64

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stree/Splitter.py
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@@ -202,7 +202,8 @@ class Splitter:
max_features < num_features). Supported strategies are: “best”: sklearn
SelectKBest algorithm is used in every node to choose the max_features
best features. “random”: The algorithm generates 5 candidates and
choose the best (max. info. gain) of them. "mutual": Chooses the best
choose the best (max. info. gain) of them. “trandom”: The algorithm
generates only one random combination. "mutual": Chooses the best
features w.r.t. their mutual info with the label. "cfs": Apply
Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
Based, by default None

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stree/Strees.py
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@@ -82,7 +82,8 @@ class Stree(BaseEstimator, ClassifierMixin):
max_features < num_features). Supported strategies are: “best”: sklearn
SelectKBest algorithm is used in every node to choose the max_features
best features. “random”: The algorithm generates 5 candidates and
choose the best (max. info. gain) of them. "mutual": Chooses the best
choose the best (max. info. gain) of them. “trandom”: The algorithm
generates only one random combination. "mutual": Chooses the best
features w.r.t. their mutual info with the label. "cfs": Apply
Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
Based , by default "random"
@@ -128,7 +129,7 @@ class Stree(BaseEstimator, ClassifierMixin):
References
----------
R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class
oblique decision tree based on support vector machines.", 2021 LNAI...
oblique decision tree based on support vector machines.", 2021 LNAI 12882
"""