Implement binary/multiclass classification metric - Spherical Payoff#18970
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KaikeWesleyReis wants to merge 3 commits intoscikit-learn:mainfrom
Open
Implement binary/multiclass classification metric - Spherical Payoff#18970KaikeWesleyReis wants to merge 3 commits intoscikit-learn:mainfrom
KaikeWesleyReis wants to merge 3 commits intoscikit-learn:mainfrom
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Hi Kaike, you might need to substantiate how widely this is used. It gets
very few hits as a web search term for me, whether as "spherical payoff" or
as spherical "scoring rule". We focus our scope and our maintenance effort
on popular, well-cited algorithms
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Author
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Hi @jnothman, Besides that, spherical payoff cousin, Brier score (they came from the same scoring family), will probably have your multi class version here. Summarizing, I understand your POV and thanks for the report! |
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What does this implement? Explain your changes.
This PR implements spherical payoff score for binary/multiclass classification.
This metric is most used in ecological modelling for Decision Making models such as Bayesian Networks, where you want to maximize the correct predicted classes and the model's confidence in that output.
It's measures the model's confidence, through the probability, to predict the correct category and have a defined interval: [0, 1]. Best possible score is 1.0, and the worst is 0.0. It's calculated as the average over all samples.
The benefits to use this score:
Any other comments?
The equation was implemented based on this 2007 paper reference:
Other point is the fact that was not implemented yet for open source community, the only reference that I found was in a paid program call Netica in this tutorial.
Abs,