File Content: plot_iris_logistic.py #!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= Logistic Regression 3-class Classifier ========================================================= Show below is a logistic-regression classifiers decision boundaries on the `iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The datapoints are colored according to their labels. """ print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. Y = iris.target h = .02 # step size in the mesh logreg = linear_model.LogisticRegression(C=1e5) # we create an instance of Neighbours Classifier and fit the data. logreg.fit(X, Y) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure(1, figsize=(4, 3)) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired) plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xticks(()) plt.yticks(()) plt.show() Submit