.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_multi_metric_evaluation.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 or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py: ============================================================================ Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV ============================================================================ Multiple metric parameter search can be done by setting the ``scoring`` parameter to a list of metric scorer names or a dict mapping the scorer names to the scorer callables. The scores of all the scorers are available in the ``cv_results_`` dict at keys ending in ``'_'`` (``'mean_test_precision'``, ``'rank_test_precision'``, etc...) The ``best_estimator_``, ``best_index_``, ``best_score_`` and ``best_params_`` correspond to the scorer (key) that is set to the ``refit`` attribute. .. GENERATED FROM PYTHON SOURCE LINES 18-30 .. code-block:: Python # Author: Raghav RV # License: BSD import numpy as np from matplotlib import pyplot as plt from sklearn.datasets import make_hastie_10_2 from sklearn.metrics import accuracy_score, make_scorer from sklearn.model_selection import GridSearchCV from sklearn.tree import DecisionTreeClassifier .. GENERATED FROM PYTHON SOURCE LINES 31-34 Running ``GridSearchCV`` using multiple evaluation metrics ---------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 34-57 .. code-block:: Python X, y = make_hastie_10_2(n_samples=8000, random_state=42) # The scorers can be either one of the predefined metric strings or a scorer # callable, like the one returned by make_scorer scoring = {"AUC": "roc_auc", "Accuracy": make_scorer(accuracy_score)} # Setting refit='AUC', refits an estimator on the whole dataset with the # parameter setting that has the best cross-validated AUC score. # That estimator is made available at ``gs.best_estimator_`` along with # parameters like ``gs.best_score_``, ``gs.best_params_`` and # ``gs.best_index_`` gs = GridSearchCV( DecisionTreeClassifier(random_state=42), param_grid={"min_samples_split": range(2, 403, 20)}, scoring=scoring, refit="AUC", n_jobs=2, return_train_score=True, ) gs.fit(X, y) results = gs.cv_results_ .. GENERATED FROM PYTHON SOURCE LINES 58-60 Plotting the result ------------------- .. GENERATED FROM PYTHON SOURCE LINES 60-117 .. code-block:: Python plt.figure(figsize=(13, 13)) plt.title("GridSearchCV evaluating using multiple scorers simultaneously", fontsize=16) plt.xlabel("min_samples_split") plt.ylabel("Score") ax = plt.gca() ax.set_xlim(0, 402) ax.set_ylim(0.73, 1) # Get the regular numpy array from the MaskedArray X_axis = np.array(results["param_min_samples_split"].data, dtype=float) for scorer, color in zip(sorted(scoring), ["g", "k"]): for sample, style in (("train", "--"), ("test", "-")): sample_score_mean = results["mean_%s_%s" % (sample, scorer)] sample_score_std = results["std_%s_%s" % (sample, scorer)] ax.fill_between( X_axis, sample_score_mean - sample_score_std, sample_score_mean + sample_score_std, alpha=0.1 if sample == "test" else 0, color=color, ) ax.plot( X_axis, sample_score_mean, style, color=color, alpha=1 if sample == "test" else 0.7, label="%s (%s)" % (scorer, sample), ) best_index = np.nonzero(results["rank_test_%s" % scorer] == 1)[0][0] best_score = results["mean_test_%s" % scorer][best_index] # Plot a dotted vertical line at the best score for that scorer marked by x ax.plot( [ X_axis[best_index], ] * 2, [0, best_score], linestyle="-.", color=color, marker="x", markeredgewidth=3, ms=8, ) # Annotate the best score for that scorer ax.annotate("%0.2f" % best_score, (X_axis[best_index], best_score + 0.005)) plt.legend(loc="best") plt.grid(False) plt.show() .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_multi_metric_evaluation_001.png :alt: GridSearchCV evaluating using multiple scorers simultaneously :srcset: /auto_examples/model_selection/images/sphx_glr_plot_multi_metric_evaluation_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 13.136 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_multi_metric_evaluation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_multi_metric_evaluation.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/model_selection/plot_multi_metric_evaluation.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multi_metric_evaluation.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_multi_metric_evaluation.py ` .. include:: plot_multi_metric_evaluation.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_