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UID:pretalx-euroscipy-2023-GYYTCH@pretalx.com
DTSTART;TZID=CET:20230816T110500
DTEND;TZID=CET:20230816T113500
DESCRIPTION:When operating a classifier in a production setting (i.e. predi
 ctive phase)\, practitioners are interested in potentially two different o
 utputs: a "hard" decision used to leverage a business decision or/and a "s
 oft" decision to get a confidence score linked to each potential decision 
 (e.g. usually related to class probabilities).\n\nScikit-learn does not pr
 ovide any flexibility to go from "soft" to "hard" predictions: it uses a c
 ut-off point at a confidence score of 0.5 (or 0 when using `decision_funct
 ion`) to get class labels. However\, optimizing a classifier to get a conf
 idence score close to the true probabilities (i.e. a calibrated classifier
 ) does not guarantee to obtain accurate "hard" predictions using this heur
 istic. Reversely\, training a classifier for an optimum "hard" prediction 
 accuracy (with the cut-off constraint at 0.5) does not guarantee obtaining
  a calibrated classifier.\n\nIn this talk\, we will present a new scikit-l
 earn meta-estimator allowing us to get the best of the two worlds: a calib
 rated classifier providing optimum "hard" predictions. This meta-estimator
  will land in a future version of scikit-learn: https://github.com/scikit-
 learn/scikit-learn/pull/26120.\n\nWe will provide some insights regarding 
 the way to obtain accurate probabilities and predictions and also illustra
 te how to use in practice this model on different use cases: cost-sensitiv
 e problems and imbalanced classification problems.
DTSTAMP:20260316T211629Z
LOCATION:HS 120
SUMMARY:Get the best from your scikit-learn classifier: trusted probabiltie
 s and optimal binary decision - Guillaume Lemaitre
URL:https://pretalx.com/euroscipy-2023/talk/GYYTCH/
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