title: python sklearn plotting classification results
body: <p>I'm new to sklearn and want to interpret classification results. I'm confused that what the differences between decision surface and decision boundary are? I saw two examples showing the differences of classifiers:</p>
  
  <p>1) <a href="http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html#example-svm-plot-iris-py" rel="nofollow">http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html#example-svm-plot-iris-py</a></p>
  
  <p>2) <a href="http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html</a></p>
  
  <p>Both are used to show the difference of classifiers. But the first one used predict and the second one used predict_proba or decision function. So I'm confused.</p>