Model Prediction vs. Ground Truth
A spam detection model is designed to classify emails as either 'Spam' or 'Not Spam'. For a particular email input, the model calculates the following probabilities: Pr(y='Spam' | x) = 0.48 and Pr(y='Not Spam' | x) = 0.52. The model predicts 'Not Spam', but this turns out to be incorrect as the email was actually spam. Based on the principle of selecting the output with the maximum probability, analyze and explain why the model made this specific prediction, even though it was factually wrong.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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A classification model is given an input,
x, and must choose an output,y, from the set of possible classes {A, B, C, D}. The model's decision rule is to select the class that has the highest conditional probability,Pr(y|x). Given the following probabilities calculated by the model for the inputx, what will its final prediction be?Pr(y=A | x)= 0.15Pr(y=B | x)= 0.55Pr(y=C | x)= 0.25Pr(y=D | x)= 0.05
Model Prediction vs. Ground Truth
Analyzing a Model's Prediction Choice