Converting Model Scores to Probabilities
A machine learning model is designed to predict which of three categories (X, Y, Z) an input belongs to. Instead of probabilities, the model outputs raw, unnormalized scores. For a specific input, the model produces the following scores: Score(X) = 8, Score(Y) = 10, Score(Z) = 2. To use these outputs in a downstream process that requires a valid probability distribution, these scores must be transformed. Based on this scenario, what is the calculated probability for category Y after the correct transformation is applied?
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Normalization Factor for a Reward-Weighted Policy
A function assigns the following unnormalized scores to three possible discrete outcomes:
score(A) = 12,score(B) = 7, andscore(C) = 1. To transform these scores into a valid probability distributionP(outcome), each score must be divided by a normalization factor calculated from the sum of all scores. What is the resulting probability for outcome B,P(B)?From Model Scores to Probabilities
Converting Model Scores to Probabilities