.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py: ====================================== Decision Tree Regression with AdaBoost ====================================== A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tree regressor. As the number of boosts is increased the regressor can fit more detail. .. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_regression_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none | .. code-block:: default print(__doc__) # Author: Noel Dawe # # License: BSD 3 clause # importing necessary libraries import numpy as np import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import AdaBoostRegressor # Create the dataset rng = np.random.RandomState(1) X = np.linspace(0, 6, 100)[:, np.newaxis] y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0]) # Fit regression model regr_1 = DecisionTreeRegressor(max_depth=4) regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng) regr_1.fit(X, y) regr_2.fit(X, y) # Predict y_1 = regr_1.predict(X) y_2 = regr_2.predict(X) # Plot the results plt.figure() plt.scatter(X, y, c="k", label="training samples") plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2) plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2) plt.xlabel("data") plt.ylabel("target") plt.title("Boosted Decision Tree Regression") plt.legend() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.441 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_adaboost_regression.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_adaboost_regression.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_