title: How to calculate AUC for random forest model in sklearn?
body: <p>The label in my data is a (N by 1) vector. The label values are either 0 for negative samples or 1 for positive samples (so, it's a binary classification problem). I use the <code>.fit</code> function of sklearn and fitted a random forest on my train set. To calculate AUC for the test set I use <code>metrics.roc_auc_score (test_labels, probabilities)</code>. I'm using 
  <code>predict_proba(my_test_set)</code> to get the probabilities. However, <code>predict_proba(my_test_set)</code> returns a (N_test, 2) matrix. I saw many people use the second column of this returned matrix (<code>predict_proba(my_test_set)[:,1]</code>) and feed it to the <code>metrics.roc_auc_score</code> to calculate AUC, but why the second one? Why not the first column (<code>predict_proba(my_test_set)[:,0]</code>)?</p>