.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/decomposition/plot_pca_vs_lda.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_decomposition_plot_pca_vs_lda.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py:


=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
and petal width.

Principal Component Analysis (PCA) applied to this data identifies the
combination of attributes (principal components, or directions in the
feature space) that account for the most variance in the data. Here we
plot the different samples on the 2 first principal components.

Linear Discriminant Analysis (LDA) tries to identify attributes that
account for the most variance *between classes*. In particular,
LDA, in contrast to PCA, is a supervised method, using known class labels.

.. GENERATED FROM PYTHON SOURCE LINES 20-68



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_001.png
         :alt: PCA of IRIS dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_002.png
         :alt: LDA of IRIS dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_002.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    explained variance ratio (first two components): [0.92461872 0.05306648]






|

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.decomposition import PCA
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    iris = datasets.load_iris()

    X = iris.data
    y = iris.target
    target_names = iris.target_names

    pca = PCA(n_components=2)
    X_r = pca.fit(X).transform(X)

    lda = LinearDiscriminantAnalysis(n_components=2)
    X_r2 = lda.fit(X, y).transform(X)

    # Percentage of variance explained for each components
    print(
        "explained variance ratio (first two components): %s"
        % str(pca.explained_variance_ratio_)
    )

    plt.figure()
    colors = ["navy", "turquoise", "darkorange"]
    lw = 2

    for color, i, target_name in zip(colors, [0, 1, 2], target_names):
        plt.scatter(
            X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=0.8, lw=lw, label=target_name
        )
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.title("PCA of IRIS dataset")

    plt.figure()
    for color, i, target_name in zip(colors, [0, 1, 2], target_names):
        plt.scatter(
            X_r2[y == i, 0], X_r2[y == i, 1], alpha=0.8, color=color, label=target_name
        )
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.title("LDA of IRIS dataset")

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.188 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_lda.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/decomposition/plot_pca_vs_lda.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_pca_vs_lda.recommendations


.. only:: html

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

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