.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/preprocessing/plot_discretization.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_preprocessing_plot_discretization.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_preprocessing_plot_discretization.py:


================================================================
使用KBinsDiscretizer离散连续特征
================================================================

该示例比较了线性回归（线性模型）和决策树（基于树的模型）在对实值特征进行离散化前后的预测结果。

如离散化前的结果所示，线性模型构建速度快且相对易于解释，但只能建模线性关系，而决策树可以构建数据的更复杂模型。使线性模型在连续数据上更强大的一种方法是使用离散化（也称为分箱）。在示例中，我们对特征进行离散化并对转换后的数据进行独热编码。请注意，如果分箱不够宽，则可能会显著增加过拟合的风险，因此通常应在交叉验证下调整离散化参数。

离散化后，线性回归和决策树做出完全相同的预测。由于每个分箱内的特征是恒定的，任何模型都必须对分箱内的所有点预测相同的值。与离散化前的结果相比，线性模型变得更加灵活，而决策树变得不那么灵活。请注意，分箱特征对基于树的模型通常没有有益效果，因为这些模型可以在数据的任何地方进行分割。

.. GENERATED FROM PYTHON SOURCE LINES 13-75



.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_discretization_001.png
   :alt: Result before discretization, Result after discretization
   :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_discretization_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import KBinsDiscretizer
    from sklearn.tree import DecisionTreeRegressor

    # 构建数据集
    rnd = np.random.RandomState(42)
    X = rnd.uniform(-3, 3, size=100)
    y = np.sin(X) + rnd.normal(size=len(X)) / 3
    X = X.reshape(-1, 1)

    # 使用KBinsDiscretizer对数据集进行转换
    enc = KBinsDiscretizer(n_bins=10, encode="onehot")
    X_binned = enc.fit_transform(X)

    # 使用原始数据集进行预测
    fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(10, 4))
    line = np.linspace(-3, 3, 1000, endpoint=False).reshape(-1, 1)
    reg = LinearRegression().fit(X, y)
    ax1.plot(line, reg.predict(line), linewidth=2, color="green", label="linear regression")
    reg = DecisionTreeRegressor(min_samples_split=3, random_state=0).fit(X, y)
    ax1.plot(line, reg.predict(line), linewidth=2, color="red", label="decision tree")
    ax1.plot(X[:, 0], y, "o", c="k")
    ax1.legend(loc="best")
    ax1.set_ylabel("Regression output")
    ax1.set_xlabel("Input feature")
    ax1.set_title("Result before discretization")

    # 使用转换后的数据集进行预测
    line_binned = enc.transform(line)
    reg = LinearRegression().fit(X_binned, y)
    ax2.plot(
        line,
        reg.predict(line_binned),
        linewidth=2,
        color="green",
        linestyle="-",
        label="linear regression",
    )
    reg = DecisionTreeRegressor(min_samples_split=3, random_state=0).fit(X_binned, y)
    ax2.plot(
        line,
        reg.predict(line_binned),
        linewidth=2,
        color="red",
        linestyle=":",
        label="decision tree",
    )
    ax2.plot(X[:, 0], y, "o", c="k")
    ax2.vlines(enc.bin_edges_[0], *plt.gca().get_ylim(), linewidth=1, alpha=0.2)
    ax2.legend(loc="best")
    ax2.set_xlabel("Input feature")
    ax2.set_title("Result after discretization")

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.102 seconds)


.. _sphx_glr_download_auto_examples_preprocessing_plot_discretization.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/preprocessing/plot_discretization.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_discretization.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_