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


================
鸢尾花数据集
================
该数据集由3种不同类型的鸢尾花（山鸢尾、杂色鸢尾和维吉尼亚鸢尾）的花瓣和花萼长度组成，存储在一个150x4的numpy.ndarray中。

行表示样本，列表示：花萼长度、花萼宽度、花瓣长度和花瓣宽度。

下面的图使用了前两个特征。
有关此数据集的更多信息，请参见 `这里 <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_  。

.. GENERATED FROM PYTHON SOURCE LINES 13-18

.. code-block:: Python


    # 代码来源：Gaël Varoquaux
    # 由Jaques Grobler修改用于文档
    # SPDX许可证标识符：BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 19-21

加载鸢尾花数据集
------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-27

.. code-block:: Python


    from sklearn import datasets

    iris = datasets.load_iris()









.. GENERATED FROM PYTHON SOURCE LINES 28-30

鸢尾花数据集的散点图
--------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 30-39

.. code-block:: Python

    import matplotlib.pyplot as plt

    _, ax = plt.subplots()
    scatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target)
    ax.set(xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])
    _ = ax.legend(
        scatter.legend_elements()[0], iris.target_names, loc="lower right", title="Classes"
    )




.. image-sg:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
   :alt: plot iris dataset
   :srcset: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 40-41

散点图中的每个点代表数据集中150朵鸢尾花中的一朵，颜色表示它们各自的类型（Setosa、Versicolour 和 Virginica）。你可以看到关于Setosa类型的一个模式，它基于其短而宽的萼片很容易识别。仅考虑这两个维度，萼片宽度和长度，Versicolor和Virginica类型之间仍然存在重叠。

.. GENERATED FROM PYTHON SOURCE LINES 43-48

绘制PCA表示
-------------------------
让我们对鸢尾花数据集应用主成分分析（PCA），
然后在前三个PCA维度上绘制鸢尾花。
这将使我们更好地区分这三种类型！

.. GENERATED FROM PYTHON SOURCE LINES 48-76

.. code-block:: Python


    # 未使用但需要导入以使用低于3.2版本的matplotlib进行3D投影
    import mpl_toolkits.mplot3d  # noqa: F401

    from sklearn.decomposition import PCA

    fig = plt.figure(1, figsize=(8, 6))
    ax = fig.add_subplot(111, projection="3d", elev=-150, azim=110)

    X_reduced = PCA(n_components=3).fit_transform(iris.data)
    ax.scatter(
        X_reduced[:, 0],
        X_reduced[:, 1],
        X_reduced[:, 2],
        c=iris.target,
        s=40,
    )

    ax.set_title("First three PCA dimensions")
    ax.set_xlabel("1st Eigenvector")
    ax.xaxis.set_ticklabels([])
    ax.set_ylabel("2nd Eigenvector")
    ax.yaxis.set_ticklabels([])
    ax.set_zlabel("3rd Eigenvector")
    ax.zaxis.set_ticklabels([])

    plt.show()




.. image-sg:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_002.png
   :alt: First three PCA dimensions
   :srcset: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 77-78

PCA 将创建 3 个新的特征，这些特征是 4 个原始特征的线性组合。此外，此转换最大化了方差。通过这种转换，我们可以看到仅使用第一个特征（即第一个特征值）就可以识别每个物种。


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

   **Total running time of the script:** (0 minutes 0.091 seconds)


.. _sphx_glr_download_auto_examples_datasets_plot_iris_dataset.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/datasets/plot_iris_dataset.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_iris_dataset.recommendations


.. only:: html

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

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