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The intent behind using the data is to predict fraudulent transactions accurately. The number of records used to train the model is exactly 1/6 of the entire annual transactions count, which was 312 million. The model is able to predict a total of 2400 records correctly as fraudulent , while it fails to sensitively predict the remaining 6600 fraudulent transactions correctly. The type 1 error count comes to be just a meagre 1% of the non-fraudulent cases present in the training data. Construct the confusion matrix using the above information. Assume fraudulent transactions as the positive class. Calculate Accuracy of the Trained Model, Misclassification rate, Precision, Sensitivity, F1-Scor

The intent behind using the data is to predict fraudulent transactions accurately. The number of records used to train the model is exactly 1/6 of the entire annual transactions count, which was 312 million. The model is able to predict a total of 2400 records correctly as fraudulent , while it fails to sensitively predict the remaining 6600 fraudulent transactions correctly. The type 1 error count comes to be just a meagre 1% of the non-fraudulent cases present in the training data. Construct the confusion matrix using the above information. Assume fraudulent transactions as the positive class. Calculate Accuracy of the Trained Model, Misclassification rate, Precision, Sensitivity, F1-Scor

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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  1. The intent behind using the data is to predict fraudulent transactions accurately. The number of records used to train the model is exactly 1/6 of the entire annual transactions count, which was 312 million. The model is able to predict a total of 2400 records correctly as fraudulent , while it fails to sensitively predict the remaining 6600 fraudulent transactions correctly. The type 1 error count comes to be just a meagre 1% of the non-fraudulent cases present in the training data. Construct the confusion matrix using the above information. Assume fraudulent transactions as the positive class. Calculate Accuracy of the Trained Model, Misclassification rate, Precision, Sensitivity, F1-Score
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