Pushing the docs for revision for branch: latex, commit 91df00f0c39621c525bc74d55d4edc3e078f01d3

Author: vighneshbirodkar

dev/_downloads/plot_kernel_ridge_regression.py

@@ -100,9 +100,8 @@
 #############################################################################
 # look at the results
 sv_ind = svr.best_estimator_.support_
-plt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',
-            zorder=2)
-plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1)
+plt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors')
+plt.scatter(X[:100], y[:100], c='k', label='data')
 plt.hold('on')
 plt.plot(X_plot, y_svr, c='r',
          label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))

dev/_downloads/plot_svm_anova.py

@@ -3,8 +3,8 @@
 SVM-Anova: SVM with univariate feature selection
 =================================================
 
-This example shows how to perform univariate feature selection before running a
-SVC (support vector classifier) to improve the classification scores.
+This example shows how to perform univariate feature before running a SVC
+(support vector classifier) to improve the classification scores.
 """
 print(__doc__)