Added DET curve to classification metrics. #4980
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Can you please add a test, update the ROC curve example to compare the two and add a new section in the narrative documentation after the ROC curve section: http://scikit-learn.org/stable/modules/model_evaluation.html#receiver-operating-characteristic-roc It would be great to teach the user how to "read" the curve and give the pro and cons of the DET curve vs ROC curve and PR curve. |
sklearn/metrics/ranking.py
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| @@ -176,6 +177,83 @@ def _binary_average_precision(y_true, y_score, sample_weight=None): | |||
| return _average_binary_score(_binary_average_precision, y_true, y_score, | |||
| average, sample_weight=sample_weight) | |||
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pep8: 2 blank lines to separate top level blocks.
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Hi Daniel. you re welcome to take it over! Thanks for asking :)
…On Sun, 10 Dec 2017, 17:11 Daniel Mohns, ***@***.***> wrote:
Hello @jkarnows <https://github.com/jkarnows> @jucor
<https://github.com/jucor> Is anyone still working on this? If not, I
might give it a shot.
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