Predicting Good Probabilities With Supervised Learning
Predicting Good Probabilities With Supervised Learning also justified for boosted trees and boosted stumps. Let the output of a learning method be f(x). To get cali-brated probabilities, pass the output through a sigmoid: P(y = 1jf) = 1 1+exp(Af +B) (1) where the parameters A and B are fitted using maximum
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