81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
"""
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======================================
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Decision Tree Regression with AdaBoost
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======================================
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A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D
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sinusoidal dataset with a small amount of Gaussian noise.
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299 boosts (300 decision trees) is compared with a single decision tree
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regressor. As the number of boosts is increased the regressor can fit more
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detail.
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See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
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example showcasing the benefits of using more efficient regression models such
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as :class:`~ensemble.HistGradientBoostingRegressor`.
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.. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
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<https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_
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"""
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# %%
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# Preparing the data
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# ------------------
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# First, we prepare dummy data with a sinusoidal relationship and some gaussian noise.
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# Author: Noel Dawe <noel.dawe@gmail.com>
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#
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# License: BSD 3 clause
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import numpy as np
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rng = np.random.RandomState(1)
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X = np.linspace(0, 6, 100)[:, np.newaxis]
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y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
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# %%
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# Training and prediction with DecisionTree and AdaBoost Regressors
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# -----------------------------------------------------------------
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# Now, we define the classifiers and fit them to the data.
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# Then we predict on that same data to see how well they could fit it.
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# The first regressor is a `DecisionTreeRegressor` with `max_depth=4`.
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# The second regressor is an `AdaBoostRegressor` with a `DecisionTreeRegressor`
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# of `max_depth=4` as base learner and will be built with `n_estimators=300`
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# of those base learners.
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from sklearn.ensemble import AdaBoostRegressor
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from sklearn.tree import DecisionTreeRegressor
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regr_1 = DecisionTreeRegressor(max_depth=4)
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regr_2 = AdaBoostRegressor(
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DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng
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)
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regr_1.fit(X, y)
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regr_2.fit(X, y)
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y_1 = regr_1.predict(X)
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y_2 = regr_2.predict(X)
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# %%
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# Plotting the results
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# --------------------
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# Finally, we plot how well our two regressors,
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# single decision tree regressor and AdaBoost regressor, could fit the data.
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import matplotlib.pyplot as plt
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import seaborn as sns
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colors = sns.color_palette("colorblind")
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plt.figure()
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plt.scatter(X, y, color=colors[0], label="training samples")
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plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)
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plt.plot(X, y_2, color=colors[2], label="n_estimators=300", linewidth=2)
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plt.xlabel("data")
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plt.ylabel("target")
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plt.title("Boosted Decision Tree Regression")
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plt.legend()
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plt.show()
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