[MRG] Ensure that ROC curve starts at (0, 0) #10093
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Looks good at a glance. Please add to what's new. Can this change affect the auc? If so, document carefully. Also, perhaps add that reference on roc analysis to the docs |
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@jnothman Thanks a lot for the instant review :) |
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LGTM |
jnothman
approved these changes
Nov 9, 2017
| @@ -160,6 +161,11 @@ Metrics | |||
| - Fixed a bug due to floating point error in :func:`metrics.roc_auc_score` with | |||
| non-integer sample weights. :issue:`9786` by :user:`Hanmin Qin <qinhanmin2014>`. | |||
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| - Fixed a bug where :func:`metrics.roc_curve` sometimes starts on y-axis instead | |||
| of (0, 0), which is inconsistent with the document and other implementations. | |||
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Note that this does not affect auc
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@jnothman Thanks. Comment addressed. |
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I don't think this is controversial: I'll take Joan's +1 on this... Let's merge. Thanks! |
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Reference Issues/PRs
Fixes #9790
See also #9850
What does this implement/fix? Explain your changes.
Currently, when the first point of ROC curve is on y-axis, we don't add a point (0, 0), which is not consistent with doc & some papers & some R packages.
Reference:
(1)scikit-learn doc
thresholds : array, shape = [n_thresholds]
Decreasing thresholds on the decision function used to compute fpr and tpr. thresholds[0] represents no instances being predicted and is arbitrarily set to max(y_score) + 1.
(2)@jnothman's comment
our concern should be that in all cases, the curve includes a point that represents predicting nothing in the positive class, and that every further point represents predicting more than nothing, for every threshold at which this changes the fpr or tpr, until all are predicted.
(3)An introduction to ROC analysis cite >7000 link
(4)R package ROCR
Any other comments?