Metadata-Version: 2.1
Name: multi-imbalance
Version: 0.0.7
Summary: Python package for tackling multiclass imbalance problems.
Home-page: https://github.com/damian-horna/multi-imbalance
Author: Damian Horna, Kamil Pluciński, Hanna Klimczak, Jacek Grycza
Author-email: horna.damian@gmail.com
License: UNKNOWN
Description: [![Build Status](https://travis-ci.org/damian-horna/multi-imbalance.svg?branch=master)](https://travis-ci.org/damian-horna/multi-imbalance)
        
        # multi-imbalance
        
        multi-imbalance is a python package tackling the problem of multi-class imbalanced datasets in machine learning.
        
        ## Requirements
        Tha package has been tested under python 3.7. Relies heavily on scikit-learn and typical scientific stack (numpy, scipy, pandas etc.).
        
        ## Installation
        Just type in
        ```bash
        pip install multi-imbalance
        ```
        
        ## Implemented algorithms
            
        1. SOUP, MDO
        2. ECOC
        3. Roughly Balanced Bagging
        4. SPIDER3 algorithm implementation for selective preprocessing of multi-class imbalanced data sets, according to article:
        
            Wojciechowski, S., Wilk, S., Stefanowski, J.: An Algorithm for Selective Preprocessing
            of Multi-class Imbalanced Data. Proceedings of the 10th International Conference
            on Computer Recognition Systems CORES 2017
        
        ## Example usage
        ```python
        from multi_imbalance.resampling.mdo import MDO
        
        # Mahalanbois Distance Oversampling
        mdo = MDO(k=9, k1_frac=0, seed=0)
        
        # read the data
        X_train, y_train, X_test, y_test = ...
        
        # preprocess
        X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))
        
        # train the classifier on preprocessed data
        clf_tree = DecisionTreeClassifier(random_state=0)
        clf_tree.fit(X_train_resampled, y_train_resampled)
        
        # make predictions
        y_pred = clf_tree.predict(X_test)
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
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