I am trying to compute area under the ROC curve using sklearn.metrics.roc_auc_score using the following method:
roc_auc = sklearn.metrics.roc_auc_score(actual, predicted)
where actual is a binary vector with ground truth classification labels and predicted is a binary vector with classification labels that my classifier has predicted.
However, the value of roc_auc that I am getting is EXACTLY similar to accuracy values (proportion of samples whose labels are correctly predicted). This is not a one-off thing. I try my classifier on various values of the parameters and every time I get the same result.
What am I doing wrong here?
This is because you are passing in the decisions of you classifier instead of the scores it calculated. There was a question on this on SO recently and a related pull request to scikit-learn.
The point of a ROC curve (and the area under it) is that you study the precision-recall tradeoff as the classification threshold is varied. By default in a binary classification task, if your classifier's score is > 0.5, then class1 is predicted, otherwise class0 is predicted. As you change that threshold, you get a curve like this. The higher up the curve is (more area under it), the better that classifier. However, to get this curve you need access to the scores of a classifier, not its decisions. Otherwise whatever the decision threshold is, the decision stay the same, and AUC degenerates to accuracy.
Which classifier are you using?
来源：https://stackoverflow.com/questions/22318922/scikit-learn-roc-auc-score-returns-accuracy-values