.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_ransac.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_linear_model_plot_ransac.py: =========================================== Robust linear model estimation using RANSAC =========================================== In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm. .. GENERATED FROM PYTHON SOURCE LINES 10-60 .. image:: /auto_examples/linear_model/images/sphx_glr_plot_ransac_001.png :alt: plot ransac :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Estimated coefficients (true, linear regression, RANSAC): 82.1903908407869 [54.17236387] [82.08533159] | .. code-block:: default import numpy as np from matplotlib import pyplot as plt from sklearn import linear_model, datasets n_samples = 1000 n_outliers = 50 X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1, n_informative=1, noise=10, coef=True, random_state=0) # Add outlier data np.random.seed(0) X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1)) y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers) # Fit line using all data lr = linear_model.LinearRegression() lr.fit(X, y) # Robustly fit linear model with RANSAC algorithm ransac = linear_model.RANSACRegressor() ransac.fit(X, y) inlier_mask = ransac.inlier_mask_ outlier_mask = np.logical_not(inlier_mask) # Predict data of estimated models line_X = np.arange(X.min(), X.max())[:, np.newaxis] line_y = lr.predict(line_X) line_y_ransac = ransac.predict(line_X) # Compare estimated coefficients print("Estimated coefficients (true, linear regression, RANSAC):") print(coef, lr.coef_, ransac.estimator_.coef_) lw = 2 plt.scatter(X[inlier_mask], y[inlier_mask], color='yellowgreen', marker='.', label='Inliers') plt.scatter(X[outlier_mask], y[outlier_mask], color='gold', marker='.', label='Outliers') plt.plot(line_X, line_y, color='navy', linewidth=lw, label='Linear regressor') plt.plot(line_X, line_y_ransac, color='cornflowerblue', linewidth=lw, label='RANSAC regressor') plt.legend(loc='lower right') plt.xlabel("Input") plt.ylabel("Response") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.152 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_ransac.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/linear_model/plot_ransac.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ransac.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ransac.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_