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


================================
最近邻分类
================================

这个示例展示了如何使用 :class:`~sklearn.neighbors.KNeighborsClassifier` 。
我们在鸢尾花数据集上训练这样的分类器，并观察与参数 `weights` 相关的决策边界的差异。

.. GENERATED FROM PYTHON SOURCE LINES 11-15

加载数据
-------------

在这个例子中，我们使用了鸢尾花数据集。我们将数据分为训练集和测试集。

.. GENERATED FROM PYTHON SOURCE LINES 15-23

.. code-block:: Python

    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    iris = load_iris(as_frame=True)
    X = iris.data[["sepal length (cm)", "sepal width (cm)"]]
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)








.. GENERATED FROM PYTHON SOURCE LINES 24-30

K-近邻分类器
------------------------------

我们希望使用一个k近邻分类器，考虑11个数据点的邻域。由于我们的k近邻模型使用欧几里得距离来寻找最近的邻居，因此在此之前对数据进行缩放是很重要的。有关更详细的信息，请参阅标题为 :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` 的示例。

因此，我们使用 :class:`~sklearn.pipeline.Pipeline` 在使用分类器之前链接一个缩放器。

.. GENERATED FROM PYTHON SOURCE LINES 30-38

.. code-block:: Python

    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler

    clf = Pipeline(
        steps=[("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=11))]
    )








.. GENERATED FROM PYTHON SOURCE LINES 39-43

决策边界
-----------------

现在，我们使用不同的 `weights` 参数值来拟合两个分类器。我们绘制每个分类器的决策边界以及原始数据集，以观察它们之间的差异。

.. GENERATED FROM PYTHON SOURCE LINES 43-75

.. code-block:: Python

    import matplotlib.pyplot as plt

    from sklearn.inspection import DecisionBoundaryDisplay

    _, axs = plt.subplots(ncols=2, figsize=(12, 5))

    for ax, weights in zip(axs, ("uniform", "distance")):
        clf.set_params(knn__weights=weights).fit(X_train, y_train)
        disp = DecisionBoundaryDisplay.from_estimator(
            clf,
            X_test,
            response_method="predict",
            plot_method="pcolormesh",
            xlabel=iris.feature_names[0],
            ylabel=iris.feature_names[1],
            shading="auto",
            alpha=0.5,
            ax=ax,
        )
        scatter = disp.ax_.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, edgecolors="k")
        disp.ax_.legend(
            scatter.legend_elements()[0],
            iris.target_names,
            loc="lower left",
            title="Classes",
        )
        _ = disp.ax_.set_title(
            f"3-Class classification\n(k={clf[-1].n_neighbors}, weights={weights!r})"
        )

    plt.show()




.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
   :alt: 3-Class classification (k=11, weights='uniform'), 3-Class classification (k=11, weights='distance')
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 76-82

Conclusion
----------

我们观察到参数 `weights` 对决策边界有影响。当 `weights="uniform"` 时，所有最近邻对决策的影响相同。而当 `weights="distance"` 时，每个邻居的权重与该邻居到查询点距离的倒数成正比。

在某些情况下，考虑距离可能会改进模型。


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

   **Total running time of the script:** (0 minutes 0.273 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_classification.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/neighbors/plot_classification.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_classification.recommendations


.. only:: html

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

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