Talk:Probabilistic classification
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 | The content of Class membership probabilities was merged into Probabilistic classification. The former page's history now serves to provide attribution for that content in the latter page, and it must not be deleted as long as the latter page exists. For the discussion at that location, see its talk page. |
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Proposed merge with Class membership probabilities
[edit]Both articles discuss the exact same problem. QVVERTYVS (hm?) 15:12, 5 May 2014 (UTC)
- Done a long time ago. QVVERTYVS (hm?) 09:42, 24 July 2015 (UTC)
Software Implementation
[edit]It would be more useful for applied work to reference the implementation in the commonly used Python language Scikit-learn package.
"Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to, say, 0.8, approximately 80% actually belong to the positive class."
https://scikit-learn.org/stable/modules/calibration.html#
Specifically, "sklearn.calibration.CalibratedClassifierCV" which provides "Probability calibration with isotonic regression or logistic regression."
https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
"The sigmoid regressor, method="sigmoid" is based on Platt’s logistic model"
https://scikit-learn.org/stable/modules/calibration.html#calibration
© 2007 - 2023, scikit-learn developers (BSD License)
Citing scikit-learn
[edit]If you use scikit-learn in a scientific publication, we would appreciate citations to the following paper:
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
Bibtex entry: @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} }