.. _sphx_glr_auto_examples_applications_plot_multi_class_under_sampling.py:


=============================================
Multiclass classification with under-sampling
=============================================

Some balancing methods allow for balancing dataset with multiples classes.
We provide an example to illustrate the use of those methods which do
not differ from the binary case.






.. rst-class:: sphx-glr-script-out

 Out::

    pre       rec       spe        f1       geo       iba       sup

              0       1.00      1.00      1.00      1.00      1.00      1.00         8
              1       1.00      0.73      1.00      0.84      0.93      0.88        11
              2       0.80      1.00      0.84      0.89      0.89      0.78        12

    avg / total       0.92      0.90      0.94      0.90      0.94      0.87        31




|


.. code-block:: python


    # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
    # License: MIT

    from sklearn.datasets import load_iris
    from sklearn.svm import LinearSVC
    from sklearn.model_selection import train_test_split

    from imblearn.under_sampling import NearMiss
    from imblearn.pipeline import make_pipeline
    from imblearn.metrics import classification_report_imbalanced

    print(__doc__)

    RANDOM_STATE = 42

    # Create a folder to fetch the dataset
    iris = load_iris()
    # Make the dataset imbalanced
    # Select only half of the first class
    iris.data = iris.data[25:-1, :]
    iris.target = iris.target[25:-1]

    X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
                                                        random_state=RANDOM_STATE)

    # Create a pipeline
    pipeline = make_pipeline(NearMiss(version=2, random_state=RANDOM_STATE),
                             LinearSVC(random_state=RANDOM_STATE))
    pipeline.fit(X_train, y_train)

    # Classify and report the results
    print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))

**Total running time of the script:** ( 0 minutes  0.294 seconds)



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     :download:`Download Python source code: plot_multi_class_under_sampling.py <plot_multi_class_under_sampling.py>`



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     :download:`Download Jupyter notebook: plot_multi_class_under_sampling.ipynb <plot_multi_class_under_sampling.ipynb>`

.. rst-class:: sphx-glr-signature

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