.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_ols_ridge_variance.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_linear_model_plot_ols_ridge_variance.py: ========================================================= Ordinary Least Squares and Ridge Regression Variance ========================================================= Due to the few points in each dimension and the straight line that linear regression uses to follow these points as well as it can, noise on the observations will cause great variance as shown in the first plot. Every line's slope can vary quite a bit for each prediction due to the noise induced in the observations. Ridge regression is basically minimizing a penalised version of the least-squared function. The penalising `shrinks` the value of the regression coefficients. Despite the few data points in each dimension, the slope of the prediction is much more stable and the variance in the line itself is greatly reduced, in comparison to that of the standard linear regression .. GENERATED FROM PYTHON SOURCE LINES 21-65 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_001.png :alt: ols :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_002.png :alt: ridge :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_002.png :class: sphx-glr-multi-img .. code-block:: Python # Code source: Gaƫl Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn import linear_model X_train = np.c_[0.5, 1].T y_train = [0.5, 1] X_test = np.c_[0, 2].T np.random.seed(0) classifiers = dict( ols=linear_model.LinearRegression(), ridge=linear_model.Ridge(alpha=0.1) ) for name, clf in classifiers.items(): fig, ax = plt.subplots(figsize=(4, 3)) for _ in range(6): this_X = 0.1 * np.random.normal(size=(2, 1)) + X_train clf.fit(this_X, y_train) ax.plot(X_test, clf.predict(X_test), color="gray") ax.scatter(this_X, y_train, s=3, c="gray", marker="o", zorder=10) clf.fit(X_train, y_train) ax.plot(X_test, clf.predict(X_test), linewidth=2, color="blue") ax.scatter(X_train, y_train, s=30, c="red", marker="+", zorder=10) ax.set_title(name) ax.set_xlim(0, 2) ax.set_ylim((0, 1.6)) ax.set_xlabel("X") ax.set_ylabel("y") fig.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.253 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_ols_ridge_variance.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/linear_model/plot_ols_ridge_variance.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/linear_model/plot_ols_ridge_variance.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ols_ridge_variance.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ols_ridge_variance.py ` .. include:: plot_ols_ridge_variance.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_