vighneshbirodkar
diff --git a/‎dev/_downloads/plot_kernel_ridge_regression.py
Lines changed: 3 additions & 2 deletions b/‎dev/_downloads/plot_kernel_ridge_regression.py
Lines changed: 3 additions & 2 deletions
diff --git a/‎dev/_images/plot_agglomerative_clustering.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering1.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_001.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_0011.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_002.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_0021.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_003.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_0031.png
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diff --git a/‎dev/_images/plot_agglomerative_clustering_004.png
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@@ -100,8 +100,9 @@
 #############################################################################
 # 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')
-plt.scatter(X[:100], y[:100], c='k', label='data')
+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.hold('on')
 plt.plot(X_plot, y_svr, c='r',
          label='SVR (fit: %.3fs, predict: %.3fs)' % (svr_fit, svr_predict))