Bagging Classifier with Under Sampling.
This approach is good for classification imbalanced data.
You can use both of Binary or Multi-Class Classification.
Methods could use looks like sci-kit learn's APIs.
Only use in python 3.x
- base_estimator : object
Classifier looks like sklearn.XXClassifier.
Classifier must have methods [fit(X, y), predict(X)].
It is not nesessary predict_proba(X), but if it has this method,
you could select 'soft voting' option and get predict probability. - n_estimators : int (default=10)
The number of base estimators. - voting : str {'hard','soft'} (default='hard')
hard : use majority rule voting
soft : argmax of the sums of prediction probabilities - n_jobs : int (default=1)
number of jobs to run in parallel for fit.
If -1, equals to number of cores.
- fit(X, y)
X : pandas.DataFrame
y : pandas.Series
return : None - predict(X)
X : pandas.DataFrame
return : predicted y : numpy.array - predict_proba(X)
X : pandas.DataFrame return : predicted probabilities (mean of all bagged models)
This software is released under the MIT License, see LICENSE