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fou are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. I thart of predicted class vs. actual class is: Actual class: 1 Actual class: 0 Predicted class: 1 85 890 Predicted class: 0 15 10 For reference: Accuracy (true positives + true negatives) / (total examples) Precision = (true positives) / (true positives + false positives) Recall = (true positives) / (true positives + false negatives) 1 score = (2 * precision * recall) / (precision + recall) What is the classifier's F1 score (as a value from 0 to 11? Write all stens

fou are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. I thart of predicted class vs. actual class is: Actual class: 1 Actual class: 0 Predicted class: 1 85 890 Predicted class: 0 15 10 For reference: Accuracy (true positives + true negatives) / (total examples) Precision = (true positives) / (true positives + false positives) Recall = (true positives) / (true positives + false negatives) 1 score = (2 * precision * recall) / (precision + recall) What is the classifier's F1 score (as a value from 0 to 11? Write all stens

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Actual class: 1 Actual class: 0 Predicted class: 1 85 890 Predicted class: 0 15 10 For reference: Accuracy = (true positives + true negatives) / (total examples) Precision = (true positives) / (true positives + false positives) Recall = (true positives) / (true positives + false negatives) F1 score = (2 * precision * recall) / (precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps
Transcribed Image Text:You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Actual class: 1 Actual class: 0 Predicted class: 1 85 890 Predicted class: 0 15 10 For reference: Accuracy = (true positives + true negatives) / (total examples) Precision = (true positives) / (true positives + false positives) Recall = (true positives) / (true positives + false negatives) F1 score = (2 * precision * recall) / (precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps
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