Confusion Matrix Report

.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "checks_gallery/tabular/model_evaluation/plot_confusion_matrix_report.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_checks_gallery_tabular_model_evaluation_plot_confusion_matrix_report.py: .. _plot_tabular_confusion_matrix_report: Confusion Matrix Report *********************** This notebook provides an overview for using and understanding the Confusion Matrix Report check. **Structure:** * `What is the Confusion Matrix Report? <#what-is-the-confusion-matrix-report>`__ * `Generate data & model <#generate-data-model>`__ * `Run the check <#run-the-check>`__ What is the Confusion Matrix Report? ====================================== The ``ConfusionMatrixReport`` produces a confusion matrix visualization which summarizes the performance of the model. The confusion matrix contains the TP (true positive), FP (false positive), TN (true negative) and FN (false negative), from which we can derive the relevant metrics, such as accuracy, precision, recall etc. (`confusion matrix `__). .. GENERATED FROM PYTHON SOURCE LINES 26-28 Imports ========= .. GENERATED FROM PYTHON SOURCE LINES 28-37 .. code-block:: default import pandas as pd from sklearn.datasets import load_iris from sklearn.ensemble import AdaBoostClassifier from sklearn.model_selection import train_test_split from deepchecks.tabular import Dataset from deepchecks.tabular.checks import ConfusionMatrixReport .. GENERATED FROM PYTHON SOURCE LINES 38-40 Generate data & model ======================= .. GENERATED FROM PYTHON SOURCE LINES 40-52 .. code-block:: default iris = load_iris(as_frame=True) clf = AdaBoostClassifier() frame = iris.frame X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) clf.fit(X_train, y_train) ds = Dataset(pd.concat([X_test, y_test], axis=1), features=iris.feature_names, label='target') .. GENERATED FROM PYTHON SOURCE LINES 53-55 Run the check =============== .. GENERATED FROM PYTHON SOURCE LINES 55-58 .. code-block:: default check = ConfusionMatrixReport() check.run(ds, clf) .. raw:: html

.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.128 seconds) .. _sphx_glr_download_checks_gallery_tabular_model_evaluation_plot_confusion_matrix_report.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_confusion_matrix_report.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_confusion_matrix_report.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_