Update predict() / predict_proba() of RF to match sklearn #3609
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hcho3
added
3 - Ready for Review
JohnZed
reviewed
Mar 12, 2021
| @@ -271,7 +271,7 @@ def fit(self, X, y, convert_dtype=False): | |||
| convert_dtype=convert_dtype) | |||
| return self | |||
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| def predict(self, X, output_class=True, algo='auto', threshold=0.5, | |||
| def predict(self, X, algo='auto', threshold=0.5, | |||
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should we remove this threshold too?
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This one is fine, since predict() produces class predictions.
JohnZed
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Mar 15, 2021
|
@gpucibot merge |
This was referenced Mar 31, 2021
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Closes #3682 In #3609, I unintentionally broke the function `score()` in the random forest. This PR restores the functionality. In addition, I added `score()` to one of the unit tests to ensure that `score()` does not break again. Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - John Zedlewski (https://github.com/JohnZed) URL: #3685
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Closes #3347.
Make the
predict()andpredict_proba()functions of RF to match those in the scikit-learn RF.output_class. Instead,predict()will always produce class prediction, andpredict_proba()will always produce probability prediction. (This applies to binary and multi-class classifiers. Regressors will only havepredict().)thresholdparameter frompredict_proba().