Show below is a logistic-regression classifiers decision boundaries on the iris dataset. The datapoints are colored according to their labels.
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 | print(__doc__)# Code source: Ga Varoquaux# Modified for documentation by Jaques Grobler# License: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom sklearn import linear_model, datasets# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2] # we only take the first two features.Y = iris.targeth = .02 # step size in the meshlogreg = 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() + .5y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5xx, 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 plotZ = Z.reshape(xx.shape)plt.figure(1, figsize=(4, 3))plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)# Plot also the training pointsplt.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() |
Total running time of the script: (0 minutes 0.087 seconds)
Download Python source code:
plot_iris_logistic.py
Download IPython notebook:
plot_iris_logistic.ipynb
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