.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_sparse_logistic_regression_mnist.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_sparse_logistic_regression_mnist.py: ===================================================== MNIST classification using multinomial logistic + L1 ===================================================== Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the number of samples is significantly larger than the number of features and is able to finely optimize non-smooth objective functions which is the case with the l1-penalty. Test accuracy reaches > 0.8, while weight vectors remains *sparse* and therefore more easily *interpretable*. Note that this accuracy of this l1-penalized linear model is significantly below what can be reached by an l2-penalized linear model or a non-linear multi-layer perceptron model on this dataset. .. GENERATED FROM PYTHON SOURCE LINES 19-84 .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sparse_logistic_regression_mnist_001.png :alt: Classification vector for... :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sparse_logistic_regression_mnist_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Sparsity with L1 penalty: 74.57% Test score with L1 penalty: 0.8253 Example run in 8.656 s | .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import time import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import fetch_openml from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.utils import check_random_state # Turn down for faster convergence t0 = time.time() train_samples = 5000 # Load data from https://www.openml.org/d/554 X, y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False) random_state = check_random_state(0) permutation = random_state.permutation(X.shape[0]) X = X[permutation] y = y[permutation] X = X.reshape((X.shape[0], -1)) X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=train_samples, test_size=10000 ) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Turn up tolerance for faster convergence clf = LogisticRegression(C=50.0 / train_samples, penalty="l1", solver="saga", tol=0.1) clf.fit(X_train, y_train) sparsity = np.mean(clf.coef_ == 0) * 100 score = clf.score(X_test, y_test) # print('Best C % .4f' % clf.C_) print("Sparsity with L1 penalty: %.2f%%" % sparsity) print("Test score with L1 penalty: %.4f" % score) coef = clf.coef_.copy() plt.figure(figsize=(10, 5)) scale = np.abs(coef).max() for i in range(10): l1_plot = plt.subplot(2, 5, i + 1) l1_plot.imshow( coef[i].reshape(28, 28), interpolation="nearest", cmap=plt.cm.RdBu, vmin=-scale, vmax=scale, ) l1_plot.set_xticks(()) l1_plot.set_yticks(()) l1_plot.set_xlabel("Class %i" % i) plt.suptitle("Classification vector for...") run_time = time.time() - t0 print("Example run in %.3f s" % run_time) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.721 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.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/linear_model/plot_sparse_logistic_regression_mnist.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_sparse_logistic_regression_mnist.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sparse_logistic_regression_mnist.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_sparse_logistic_regression_mnist.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_sparse_logistic_regression_mnist.zip ` .. include:: plot_sparse_logistic_regression_mnist.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_