Multi Model Performance 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_multi_model_performance_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_multi_model_performance_report.py: .. _plot_tabular_multi_model_performance_report: Multi Model Performance Report ******************************** This notebook provides an overview for using and understanding the Multi Model Performance Report check. **Structure:** * `What is the Multi Model Performance Report check? <#what-is-the-multi-model-performance-report-check>`__ * `Multiclass check <#multiclass-check>`__ * `Regression check <#regression-check>`__ What is the Multi Model Performance Report? ============================================ The ``MultiModelPerformanceReport`` check produces a summary of performance scores for multiple models on test datasets. The default scorers that are used are F1, Precision and Recall for Classification and Negative RMSE (Root Mean Square Error), Negative MAE (Mean Absolute Error) and R2 for Regression. .. GENERATED FROM PYTHON SOURCE LINES 24-26 Multiclass check ================== .. GENERATED FROM PYTHON SOURCE LINES 28-30 Imports --------- .. GENERATED FROM PYTHON SOURCE LINES 30-39 .. code-block:: default from sklearn.datasets import load_iris from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from deepchecks.tabular import Dataset from deepchecks.tabular.checks import MultiModelPerformanceReport .. GENERATED FROM PYTHON SOURCE LINES 40-42 Generate data & model ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 42-54 .. code-block:: default iris = load_iris(as_frame=True) train, test = train_test_split(iris.frame, test_size=0.33, random_state=42) train_ds = Dataset(train, label="target") test_ds = Dataset(test, label="target") features = train_ds.data[train_ds.features] label = train_ds.data[train_ds.label_name] clf1 = AdaBoostClassifier().fit(features, label) clf2 = RandomForestClassifier().fit(features, label) clf3 = DecisionTreeClassifier().fit(features, label) .. GENERATED FROM PYTHON SOURCE LINES 55-57 Run the check ---------------- .. GENERATED FROM PYTHON SOURCE LINES 57-59 .. code-block:: default MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3]) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 60-62 Regression check ================= .. GENERATED FROM PYTHON SOURCE LINES 65-67 Imports -------- .. GENERATED FROM PYTHON SOURCE LINES 67-71 .. code-block:: default from sklearn.datasets import load_diabetes from sklearn.ensemble import AdaBoostRegressor, RandomForestRegressor from sklearn.tree import DecisionTreeRegressor .. GENERATED FROM PYTHON SOURCE LINES 72-74 Generate data & model ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 74-86 .. code-block:: default diabetes = load_diabetes(as_frame=True) train, test = train_test_split(diabetes.frame, test_size=0.33, random_state=42) train_ds = Dataset(train, label="target", cat_features=['sex']) test_ds = Dataset(test, label="target", cat_features=['sex']) features = train_ds.data[train_ds.features] label = train_ds.data[train_ds.label_name] clf1 = AdaBoostRegressor().fit(features, label) clf2 = RandomForestRegressor().fit(features, label) clf3 = DecisionTreeRegressor().fit(features, label) .. GENERATED FROM PYTHON SOURCE LINES 87-89 Run the check ---------------- .. GENERATED FROM PYTHON SOURCE LINES 89-90 .. code-block:: default MultiModelPerformanceReport().run(train_ds, test_ds, [clf1, clf2, clf3]) .. raw:: html

Multi Model Performance Report

.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.034 seconds) .. _sphx_glr_download_checks_gallery_tabular_model_evaluation_plot_multi_model_performance_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_multi_model_performance_report.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multi_model_performance_report.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_