The sklearn documentation discusses calibration. One of the example models (in orange) is a naïve Bayes model that has a descending calibration curve, meaning that observations with larger estimated probabilities of occurrence actually occur less often.
A member of this community has posted an example where the same phenomenon occurs but is even more visually extreme.
Those seem to be examples where the rankings are wrong.
The sklearn documentation discusses calibration. One of the example models (in orange) is a naïve Bayes model that has a descending calibration curve, meaning that observations with larger estimated probabilities of occurrence actually occur less often.
A member of this community has posted an example where the same phenomenon occurs but even more visually extreme.
Those seem to be examples where the rankings are wrong.
The sklearn documentation discusses calibration. One of the example models (in orange) is a naïve Bayes model that has a descending calibration curve, meaning that observations with larger estimated probabilities of occurrence actually occur less often.
A member of this community has posted an example where the same phenomenon occurs but is even more visually extreme.
Those seem to be examples where the rankings are wrong.
The sklearn documentation discusses calibration. One of the example models (in orange) is a naïve Bayes model that has a descending calibration curve, meaning that observations with larger estimated probabilities of occurrence actually occur less often.
A member of this community has posted an example where the same phenomenon occurs but even more visually extreme.
Those seem to be examples where the rankings are wrong.
