Added pos_label parameter to roc_auc_score function #2616
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… it on binary tagets that aren't {0, 1} or {-1, 1}.
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+1 for merge 👍 |
| @@ -365,7 +365,7 @@ def auc_score(y_true, y_score): | |||
| return roc_auc_score(y_true, y_score) | |||
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| def roc_auc_score(y_true, y_score): | |||
| def roc_auc_score(y_true, y_score, pos_label=None): | |||
| """Compute Area Under the Curve (AUC) from prediction scores | |||
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| Note: this implementation is restricted to the binary classification task. | |||
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yes since this pr is waiting to be merged #2460
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How does #2460 will handle binary case?
As it is at the moment, I haven't change the logic around the positive label handling.
Will it still return one value for "positive" class or return ROC for both classes (i.e. no need in pos_label in this case)?
It detects if y_true and y_score are in multilabel-indicator format. In that case, there isn't any ambiguity on the number of classes/labels. The format checking can be easily done by checking the number of dimension of y_true/y_score. Note taht It doesn't handle the problematic multiclass task.
Depending on the chosen averaging option, you will get one value for all binary tasks or one for each task.
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I think that you should have a look to the pr #2610 of @jnothman. Should we switch to a |
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closing this as no reply, also it's replaced by #6874. |
To be able to run
roc_auc_scoreon binary tagets that aren't {0, 1} or {-1, 1}.