Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the full model, it does not give us much regarding y when compared to just feature 1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | print(__doc__)# Code source: Ga Varoquaux# Modified for documentation by Jaques Grobler# License: BSD 3 clauseimport matplotlib.pyplot as pltimport numpy as npfrom mpl_toolkits.mplot3d import Axes3Dfrom sklearn import datasets, linear_modeldiabetes = datasets.load_diabetes()indices = (0, 1)X_train = diabetes.data[:-20, indices]X_test = diabetes.data[-20:, indices]y_train = diabetes.target[:-20]y_test = diabetes.target[-20:]ols = linear_model.LinearRegression()ols.fit(X_train, y_train) |
Plot the figure
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 | def plot_figs(fig_num, elev, azim, X_train, clf): fig = plt.figure(fig_num, figsize=(4, 3)) plt.clf() ax = Axes3D(fig, elev=elev, azim=azim) ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+') ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]), np.array([[-.1, .15], [-.1, .15]]), clf.predict(np.array([[-.1, -.1, .15, .15], [-.1, .15, -.1, .15]]).T ).reshape((2, 2)), alpha=.5) ax.set_xlabel('X_1') ax.set_ylabel('X_2') ax.set_zlabel('Y') ax.w_xaxis.set_ticklabels([]) ax.w_yaxis.set_ticklabels([]) ax.w_zaxis.set_ticklabels([])#Generate the three different figures from different viewselev = 43.5azim = -110plot_figs(1, elev, azim, X_train, ols)elev = -.5azim = 0plot_figs(2, elev, azim, X_train, ols)elev = -.5azim = 90plot_figs(3, elev, azim, X_train, ols)plt.show() |
Total running time of the script: (0 minutes 0.374 seconds)
Download Python source code:
plot_ols_3d.py
Download IPython notebook:
plot_ols_3d.ipynb
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