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True positive rate Accuracy Precision Recall F1 score Predicted Class Spam d1+25 Non-spam d2+10 Spam Non-spam Actual Class d2+5 dl+45

True positive rate Accuracy Precision Recall F1 score Predicted Class Spam d1+25 Non-spam d2+10 Spam Non-spam Actual Class d2+5 dl+45

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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Note: d1=5 d2=7 Subject: Artificial intelligence
True positive rate Accuracy Precision Recall i) ii) iv) v) F1 score Predicted Class Spam dl+25 Non-spam d2+10 Spam Non-spam Actual Class d2+5 dl+45
Transcribed Image Text:True positive rate Accuracy Precision Recall i) ii) iv) v) F1 score Predicted Class Spam dl+25 Non-spam d2+10 Spam Non-spam Actual Class d2+5 dl+45
Question No. 8 Let dl = last digit of your registration number and d2 = second last digit of your registration number Suppose a machine learning algorithm has been used to classify incoming email as Spam or No-spam. The following confusion matrix gives predicted classes against actual classes of emails. Calculate the following metrics:
Transcribed Image Text:Question No. 8 Let dl = last digit of your registration number and d2 = second last digit of your registration number Suppose a machine learning algorithm has been used to classify incoming email as Spam or No-spam. The following confusion matrix gives predicted classes against actual classes of emails. Calculate the following metrics:
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