.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_validation_curve.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_selection_plot_validation_curve.py: ========================== Plotting Validation Curves ========================== In this plot you can see the training scores and validation scores of an SVM for different values of the kernel parameter gamma. For very low values of gamma, you can see that both the training score and the validation score are low. This is called underfitting. Medium values of gamma will result in high values for both scores, i.e. the classifier is performing fairly well. If gamma is too high, the classifier will overfit, which means that the training score is good but the validation score is poor. .. GENERATED FROM PYTHON SOURCE LINES 14-51 .. image:: /auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png :alt: Validation Curve with SVM :class: sphx-glr-single-img .. code-block:: default print(__doc__) import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_digits from sklearn.svm import SVC from sklearn.model_selection import validation_curve X, y = load_digits(return_X_y=True) param_range = np.logspace(-6, -1, 5) train_scores, test_scores = validation_curve( SVC(), X, y, param_name="gamma", param_range=param_range, scoring="accuracy", n_jobs=1) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.title("Validation Curve with SVM") plt.xlabel(r"$\gamma$") plt.ylabel("Score") plt.ylim(0.0, 1.1) lw = 2 plt.semilogx(param_range, train_scores_mean, label="Training score", color="darkorange", lw=lw) plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color="darkorange", lw=lw) plt.semilogx(param_range, test_scores_mean, label="Cross-validation score", color="navy", lw=lw) plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color="navy", lw=lw) plt.legend(loc="best") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 15.235 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_validation_curve.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_validation_curve.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_validation_curve.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_validation_curve.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_