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


==============================================
Regularization path of L1- Logistic Regression
==============================================


Train l1-penalized logistic regression models on a binary classification
problem derived from the Iris dataset.

The models are ordered from strongest regularized to least regularized. The 4
coefficients of the models are collected and plotted as a "regularization
path": on the left-hand side of the figure (strong regularizers), all the
coefficients are exactly 0. When regularization gets progressively looser,
coefficients can get non-zero values one after the other.

Here we choose the liblinear solver because it can efficiently optimize for the
Logistic Regression loss with a non-smooth, sparsity inducing l1 penalty.

Also note that we set a low value for the tolerance to make sure that the model
has converged before collecting the coefficients.

We also use warm_start=True which means that the coefficients of the models are
reused to initialize the next model fit to speed-up the computation of the
full-path.

.. GENERATED FROM PYTHON SOURCE LINES 27-31

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 32-34

Load data
---------

.. GENERATED FROM PYTHON SOURCE LINES 34-42

.. code-block:: Python


    from sklearn import datasets

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    feature_names = iris.feature_names








.. GENERATED FROM PYTHON SOURCE LINES 43-44

Here we remove the third class to make the problem a binary classification

.. GENERATED FROM PYTHON SOURCE LINES 44-47

.. code-block:: Python

    X = X[y != 2]
    y = y[y != 2]








.. GENERATED FROM PYTHON SOURCE LINES 48-50

Compute regularization path
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 50-60

.. code-block:: Python


    import numpy as np

    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import l1_min_c

    cs = l1_min_c(X, y, loss="log") * np.logspace(0, 1, 16)








.. GENERATED FROM PYTHON SOURCE LINES 61-65

Create a pipeline with `StandardScaler` and `LogisticRegression`, to normalize
the data before fitting a linear model, in order to speed-up convergence and
make the coefficients comparable. Also, as a side effect, since the data is now
centered around 0, we don't need to fit an intercept.

.. GENERATED FROM PYTHON SOURCE LINES 65-84

.. code-block:: Python

    clf = make_pipeline(
        StandardScaler(),
        LogisticRegression(
            penalty="l1",
            solver="liblinear",
            tol=1e-6,
            max_iter=int(1e6),
            warm_start=True,
            fit_intercept=False,
        ),
    )
    coefs_ = []
    for c in cs:
        clf.set_params(logisticregression__C=c)
        clf.fit(X, y)
        coefs_.append(clf["logisticregression"].coef_.ravel().copy())

    coefs_ = np.array(coefs_)








.. GENERATED FROM PYTHON SOURCE LINES 85-87

Plot regularization path
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 87-104

.. code-block:: Python


    import matplotlib.pyplot as plt

    # Colorblind-friendly palette (IBM Color Blind Safe palette)
    colors = ["#648FFF", "#785EF0", "#DC267F", "#FE6100"]

    plt.figure(figsize=(10, 6))
    for i in range(coefs_.shape[1]):
        plt.semilogx(cs, coefs_[:, i], marker="o", color=colors[i], label=feature_names[i])

    ymin, ymax = plt.ylim()
    plt.xlabel("C")
    plt.ylabel("Coefficients")
    plt.title("Logistic Regression Path")
    plt.legend()
    plt.axis("tight")
    plt.show()



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :alt: Logistic Regression Path
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.164 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.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/1.7.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_logistic_path.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

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

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


.. include:: plot_logistic_path.recommendations


.. only:: html

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

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