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Assume we have a binary classifier, and assume the two classes are equally likely. What is the RMS calibration error of a nearly uniform random classifier, namely a classifier that, for each example, assigns probability 50% + (2. Bernoulli (0.5) - 1) to the first class? What is the RMS calibration error if it always predicts the first class and assigns the first class 100% confidence? Suppose 70% of the calibration data points belong to class 1 and that the model always predicts ŷ = 1 with p(ŷ | x) = 0.7; what is the calibration of this model?

Assume we have a binary classifier, and assume the two classes are equally likely. What is the RMS calibration error of a nearly uniform random classifier, namely a classifier that, for each example, assigns probability 50% + (2. Bernoulli (0.5) - 1) to the first class? What is the RMS calibration error if it always predicts the first class and assigns the first class 100% confidence? Suppose 70% of the calibration data points belong to class 1 and that the model always predicts ŷ = 1 with p(ŷ | x) = 0.7; what is the calibration of this model?

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Assume we have a binary classifier, and assume the two classes are equally likely. What is the RMS calibration error of a nearly uniform random classifier, namely a classifier that, for each example, assigns probability 50% + € (2. Bernoulli (0.5) - 1) to the first class? What is the RMS calibration error if it always predicts the first class and assigns the first class 100% confidence? Suppose 70% of the calibration data points belong to class 1 and that the model always predicts = 1 with p(ŷ | x) = 0.7; what is the calibration of this model?
Transcribed Image Text:Assume we have a binary classifier, and assume the two classes are equally likely. What is the RMS calibration error of a nearly uniform random classifier, namely a classifier that, for each example, assigns probability 50% + € (2. Bernoulli (0.5) - 1) to the first class? What is the RMS calibration error if it always predicts the first class and assigns the first class 100% confidence? Suppose 70% of the calibration data points belong to class 1 and that the model always predicts = 1 with p(ŷ | x) = 0.7; what is the calibration of this model?
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