.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_grid_search_refit_callable.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_grid_search_refit_callable.py: ================================================== Balance model complexity and cross-validated score ================================================== This example balances model complexity and cross-validated score by finding a decent accuracy within 1 standard deviation of the best accuracy score while minimising the number of PCA components [1]. The figure shows the trade-off between cross-validated score and the number of PCA components. The balanced case is when n_components=10 and accuracy=0.88, which falls into the range within 1 standard deviation of the best accuracy score. [1] Hastie, T., Tibshirani, R.,, Friedman, J. (2001). Model Assessment and Selection. The Elements of Statistical Learning (pp. 219-260). New York, NY, USA: Springer New York Inc.. .. GENERATED FROM PYTHON SOURCE LINES 20-128 .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_grid_search_refit_callable_001.png :alt: Balance model complexity and cross-validated score :srcset: /auto_examples/model_selection/images/sphx_glr_plot_grid_search_refit_callable_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none The best_index_ is 2 The n_components selected is 10 The corresponding accuracy score is 0.88 | .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_digits from sklearn.decomposition import PCA from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline from sklearn.svm import LinearSVC def lower_bound(cv_results): """ Calculate the lower bound within 1 standard deviation of the best `mean_test_scores`. Parameters ---------- cv_results : dict of numpy(masked) ndarrays See attribute cv_results_ of `GridSearchCV` Returns ------- float Lower bound within 1 standard deviation of the best `mean_test_score`. """ best_score_idx = np.argmax(cv_results["mean_test_score"]) return ( cv_results["mean_test_score"][best_score_idx] - cv_results["std_test_score"][best_score_idx] ) def best_low_complexity(cv_results): """ Balance model complexity with cross-validated score. Parameters ---------- cv_results : dict of numpy(masked) ndarrays See attribute cv_results_ of `GridSearchCV`. Return ------ int Index of a model that has the fewest PCA components while has its test score within 1 standard deviation of the best `mean_test_score`. """ threshold = lower_bound(cv_results) candidate_idx = np.flatnonzero(cv_results["mean_test_score"] >= threshold) best_idx = candidate_idx[ cv_results["param_reduce_dim__n_components"][candidate_idx].argmin() ] return best_idx pipe = Pipeline( [ ("reduce_dim", PCA(random_state=42)), ("classify", LinearSVC(random_state=42, C=0.01)), ] ) param_grid = {"reduce_dim__n_components": [6, 8, 10, 12, 14]} grid = GridSearchCV( pipe, cv=10, n_jobs=1, param_grid=param_grid, scoring="accuracy", refit=best_low_complexity, ) X, y = load_digits(return_X_y=True) grid.fit(X, y) n_components = grid.cv_results_["param_reduce_dim__n_components"] test_scores = grid.cv_results_["mean_test_score"] plt.figure() plt.bar(n_components, test_scores, width=1.3, color="b") lower = lower_bound(grid.cv_results_) plt.axhline(np.max(test_scores), linestyle="--", color="y", label="Best score") plt.axhline(lower, linestyle="--", color=".5", label="Best score - 1 std") plt.title("Balance model complexity and cross-validated score") plt.xlabel("Number of PCA components used") plt.ylabel("Digit classification accuracy") plt.xticks(n_components.tolist()) plt.ylim((0, 1.0)) plt.legend(loc="upper left") best_index_ = grid.best_index_ print("The best_index_ is %d" % best_index_) print("The n_components selected is %d" % n_components[best_index_]) print( "The corresponding accuracy score is %.2f" % grid.cv_results_["mean_test_score"][best_index_] ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.210 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_grid_search_refit_callable.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_grid_search_refit_callable.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/model_selection/plot_grid_search_refit_callable.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_grid_search_refit_callable.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_grid_search_refit_callable.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_grid_search_refit_callable.zip ` .. include:: plot_grid_search_refit_callable.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_