Considers the the following output from a machine learning algorithm that predicts whether an email message is spam (YES) or not (NO) on a test set of 165 messages. Predicted: Predicted: n=165 NO YES Actual: NO 50 10 Actual: YES 100 Compute the accuracy of the model (rounded to two points after the decimal points) and provide the value in the space below Answer: NO

Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter4: Eigenvalues And Eigenvectors
Section4.6: Applications And The Perron-frobenius Theorem
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Considers the the following output from a machine learning algorithm that predicts whether an email message is spam (YES) or not (NO) on a test set of 165 messages.
Predicted:
Predicted:
n=165
NO
YES
Actual:
NO
50
10
Actual:
YES
100
Compute the accuracy of the model (rounded to two points after the decimal points) and provide the value in the space below
Answer: NOd
Transcribed Image Text:Considers the the following output from a machine learning algorithm that predicts whether an email message is spam (YES) or not (NO) on a test set of 165 messages. Predicted: Predicted: n=165 NO YES Actual: NO 50 10 Actual: YES 100 Compute the accuracy of the model (rounded to two points after the decimal points) and provide the value in the space below Answer: NOd
Given the following true labels, and predicted labels output by a classification algorithm, compute the accuracy and write the final answer
rounded to two positions after the decimal point
y true (1.0,1, 1,0,1. 1,0, 1.0. 1.1. 1. 1.1.0,0, 1,0, 1, 1)
y_pred - 1. 1.1. 1.0, 1,0.0,0, 1.0.0. 1.0, 1.0,0, 1,0.1. 1)
Answer:
Transcribed Image Text:Given the following true labels, and predicted labels output by a classification algorithm, compute the accuracy and write the final answer rounded to two positions after the decimal point y true (1.0,1, 1,0,1. 1,0, 1.0. 1.1. 1. 1.1.0,0, 1,0, 1, 1) y_pred - 1. 1.1. 1.0, 1,0.0,0, 1.0.0. 1.0, 1.0,0, 1,0.1. 1) Answer:
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