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] |
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | print(__doc__)# Author: Noel Dawe <noel.dawe@gmail.com>## License: BSD 3 clause# importing necessary librariesimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.ensemble import AdaBoostRegressor# Create the datasetrng = 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 modelregr_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)# Predicty_1 = regr_1.predict(X)y_2 = regr_2.predict(X)# Plot the resultsplt.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() |
Total running time of the script: (0 minutes 0.742 seconds)
Download Python source code:
plot_adaboost_regression.py
Download IPython notebook:
plot_adaboost_regression.ipynb
Please login to continue.