.. _sphx_glr_auto_examples_evaluation_plot_classification_report.py:


=============================================
Evaluate classification by compiling a report
=============================================

Specific metrics have been developed to evaluate classifier which has been
trained using imbalanced data. `imblearn` provides a classification
report similar to `sklearn`, with additional metrics specific to imbalanced
learning problem.





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

 Out::

    pre       rec       spe        f1       geo       iba       sup

              0       0.42      0.85      0.87      0.56      0.64      0.39       123
              1       0.98      0.87      0.85      0.92      0.64      0.44      1127

    avg / total       0.93      0.87      0.85      0.89      0.64      0.43      1250




|


.. code-block:: python


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


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

    from imblearn import over_sampling as os
    from imblearn import pipeline as pl
    from imblearn.metrics import classification_report_imbalanced

    print(__doc__)

    RANDOM_STATE = 42

    # Generate a dataset
    X, y = datasets.make_classification(n_classes=2, class_sep=2,
                                        weights=[0.1, 0.9], n_informative=10,
                                        n_redundant=1, flip_y=0, n_features=20,
                                        n_clusters_per_class=4, n_samples=5000,
                                        random_state=RANDOM_STATE)

    pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
                                LinearSVC(random_state=RANDOM_STATE))

    # Split the data
    X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                        random_state=RANDOM_STATE)

    # Train the classifier with balancing
    pipeline.fit(X_train, y_train)

    # Test the classifier and get the prediction
    y_pred_bal = pipeline.predict(X_test)

    # Show the classification report
    print(classification_report_imbalanced(y_test, y_pred_bal))

**Total running time of the script:** ( 0 minutes  0.949 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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