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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | print(__doc__)# Code source: Gael Varoquaux# License: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import linear_model# this is our test set, it's just a straight line with some# Gaussian noisexmin, xmax = -5, 5n_samples = 100np.random.seed(0)X = np.random.normal(size=n_samples)y = (X > 0).astype(np.float)X[X > 0] *= 4X += .3 * np.random.normal(size=n_samples)X = X[:, np.newaxis]# run the classifierclf = linear_model.LogisticRegression(C=1e5)clf.fit(X, y)# and plot the resultplt.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.show() |
Total running time of the script: (0 minutes 0.104 seconds)
Download Python source code:
plot_logistic.py
Download IPython notebook:
plot_logistic.ipynb
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