.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/evaluation/plot_classification_report.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _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. :mod:`imblearn` provides a classification report similar to :mod:`sklearn`, with additional metrics specific to imbalanced learning problem. .. GENERATED FROM PYTHON SOURCE LINES 11-60 .. rst-class:: sphx-glr-script-out .. code-block:: none pre rec spe f1 geo iba sup 0 0.42 0.84 0.88 0.56 0.86 0.73 123 1 0.98 0.88 0.84 0.93 0.86 0.74 1127 avg / total 0.93 0.87 0.84 0.89 0.86 0.74 1250 | .. code-block:: Python # Authors: Guillaume Lemaitre # License: MIT from sklearn import datasets from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler 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( StandardScaler(), os.SMOTE(random_state=RANDOM_STATE), LogisticRegression(max_iter=10_000), ) # 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)) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.432 seconds) **Estimated memory usage:** 10 MB .. _sphx_glr_download_auto_examples_evaluation_plot_classification_report.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_classification_report.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classification_report.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_