.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_adaboost_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py>`
        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_ensemble_plot_adaboost_regression.py:


======================================
AdaBoost 决策树回归
======================================

在带有少量高斯噪声的一维正弦数据集上，使用 AdaBoost.R2 [1]_ 算法对决策树进行提升。
将 299 次提升（300 棵决策树）与单个决策树回归器进行比较。随着提升次数的增加，回归器可以拟合更多细节。

请参阅 :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` 以了解使用更高效回归模型（如 :class:`~ensemble.HistGradientBoostingRegressor` ）的好处。

.. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
        <https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_  

.. GENERATED FROM PYTHON SOURCE LINES 17-20

准备数据
--------
首先，我们准备具有正弦关系和一些高斯噪声的虚拟数据。

.. GENERATED FROM PYTHON SOURCE LINES 20-31

.. code-block:: Python



    # 作者：scikit-learn 开发者
    # SPDX许可证标识符：BSD-3-Clause

    import numpy as np

    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])








.. GENERATED FROM PYTHON SOURCE LINES 32-39

训练和预测使用决策树和AdaBoost回归器
----------------------------------------
现在，我们定义分类器并将它们拟合到数据上。
然后我们在相同的数据上进行预测，以查看它们的拟合效果。
第一个回归器是一个 `max_depth=4` 的 `DecisionTreeRegressor` 。
第二个回归器是一个以 `max_depth=4` 的 `DecisionTreeRegressor` 为基础学习器的 `AdaBoostRegressor` ，
并将使用 `n_estimators=300` 个这样的基础学习器进行构建。

.. GENERATED FROM PYTHON SOURCE LINES 39-56

.. code-block:: Python



    from sklearn.ensemble import AdaBoostRegressor
    from sklearn.tree import DecisionTreeRegressor

    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)

    y_1 = regr_1.predict(X)
    y_2 = regr_2.predict(X)








.. GENERATED FROM PYTHON SOURCE LINES 57-60

绘制结果
--------
最后，我们绘制了两个回归器（单一决策树回归器和AdaBoost回归器）拟合数据的效果。

.. GENERATED FROM PYTHON SOURCE LINES 60-75

.. code-block:: Python


    import matplotlib.pyplot as plt
    import seaborn as sns

    colors = sns.color_palette("colorblind")

    plt.figure()
    plt.scatter(X, y, color=colors[0], label="training samples")
    plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)
    plt.plot(X, y_2, color=colors[2], label="n_estimators=300", linewidth=2)
    plt.xlabel("data")
    plt.ylabel("target")
    plt.title("Boosted Decision Tree Regression")
    plt.legend()
    plt.show()



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_regression_001.png
   :alt: Boosted Decision Tree Regression
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_regression_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.197 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.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/ensemble/plot_adaboost_regression.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_adaboost_regression.ipynb <plot_adaboost_regression.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_adaboost_regression.py <plot_adaboost_regression.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_adaboost_regression.zip <plot_adaboost_regression.zip>`


.. include:: plot_adaboost_regression.recommendations


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