Metadata-Version: 1.1
Name: usbclassifier
Version: 0.1.3
Summary: Bagging Classifier with Under Sampling
Home-page: https://github.com/nekoumei/usbclassifier
Author: nekoumei
Author-email: nekoumei@gmail.com
License: MIT
Description: # USBaggingClassifier
        # Overview
        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
        # Usage
        ## Parameters
        * 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.  
        ## methods
        * 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)
        
        # LICENSE
        This software is released under the MIT License, see [LICENSE](/LICENSE)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: MacOS X
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
