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You are building a ML model to predict a four-class classifification. You have 1 million samples of all relevant attributes, including the predictor. The model predicted class 1 for all the samples correctly. When predicting class 2, the model confused class 2 with class 1,3,4 equally. When predicting class 3, the model does not confuse class 1 and class 2, but there is error with class 4. When predicting class 4, it has 10% issue with class 1, 30% error with class 2 and no errors with class 3. (a) Choose an ideal train- test data split, compute the total number of test samples and write the confusion matrix.

You are building a ML model to predict a four-class classifification. You have 1 million samples of all relevant attributes, including the predictor. The model predicted class 1 for all the samples correctly. When predicting class 2, the model confused class 2 with class 1,3,4 equally. When predicting class 3, the model does not confuse class 1 and class 2, but there is error with class 4. When predicting class 4, it has 10% issue with class 1, 30% error with class 2 and no errors with class 3. (a) Choose an ideal train- test data split, compute the total number of test samples and write the confusion matrix.

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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4690 You are building a ML model to predict a four-class classifification. You have 1 million samples of all relevant attributes, including the The model predicted class 1 for all the samples correctly. When predicting class 2, the model confused class 2 with class 1,3,4 equally. When predicting class 3, the model does not confuse class 1 and class 2, but there is error with class 4. When predicting class 4, it has 10% issue with class 1, 30% error with class 2 and no errors with class 3. (a) Choose an ideal train- test data split, compute the total number of test samples and write the confusion matrix.
Transcribed Image Text:4690 You are building a ML model to predict a four-class classifification. You have 1 million samples of all relevant attributes, including the The model predicted class 1 for all the samples correctly. When predicting class 2, the model confused class 2 with class 1,3,4 equally. When predicting class 3, the model does not confuse class 1 and class 2, but there is error with class 4. When predicting class 4, it has 10% issue with class 1, 30% error with class 2 and no errors with class 3. (a) Choose an ideal train- test data split, compute the total number of test samples and write the confusion matrix.
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