.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_linear_model_plot_logistic.py: ========================================================= Logistic function ========================================================= Shown in the plot is how the logistic regression would, in this synthetic dataset, classify values as either 0 or 1, i.e. class one or two, using the logistic curve. .. image:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none | .. code-block:: default print(__doc__) # Code source: Gael Varoquaux # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model # General a toy dataset:s it's just a straight line with some Gaussian noise: xmin, xmax = -5, 5 n_samples = 100 np.random.seed(0) X = np.random.normal(size=n_samples) y = (X > 0).astype(np.float) X[X > 0] *= 4 X += .3 * np.random.normal(size=n_samples) X = X[:, np.newaxis] # Fit the classifier clf = linear_model.LogisticRegression(C=1e5, solver='lbfgs') clf.fit(X, y) # and plot the result plt.figure(1, figsize=(4, 3)) plt.clf() plt.scatter(X.ravel(), y, color='black', zorder=20) X_test = np.linspace(-5, 10, 300) def model(x): return 1 / (1 + np.exp(-x)) loss = model(X_test * clf.coef_ + clf.intercept_).ravel() plt.plot(X_test, loss, color='red', linewidth=3) ols = linear_model.LinearRegression() ols.fit(X, y) plt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1) plt.axhline(.5, color='.5') plt.ylabel('y') plt.xlabel('X') plt.xticks(range(-5, 10)) plt.yticks([0, 0.5, 1]) plt.ylim(-.25, 1.25) plt.xlim(-4, 10) plt.legend(('Logistic Regression Model', 'Linear Regression Model'), loc="lower right", fontsize='small') plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.158 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_logistic.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_logistic.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_logistic.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_