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?
Holt Mcdougal Larson Pre-algebra: Student Edition 2012
1st Edition
ISBN:9780547587776
Author:HOLT MCDOUGAL
Publisher:HOLT MCDOUGAL
Chapter11: Data Analysis And Probability
Section: Chapter Questions
Problem 8CR
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Question
![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?](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F09d3870c-ef1d-4fe3-a6af-bd60b117be7b%2F524be975-e646-47a4-a2ea-be07b3118d9f%2F8iegs82_processed.png&w=3840&q=75)
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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