Suppose we train a model to predict whether a credit card transaction is Fraudulent or Not....
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Suppose we train a model to predict whether a credit card transaction is Fraudulent or Not. After training the model, we apply it to a test set of 200 new transactions (also labelled) and the model produces the contingency table below. Predicted Class Fraud Not Fraud True Fraud 60 Class Not Fraud 120 20 st your crisp point-wise observations on the classifier with supporting justification. (3 marks) Suppose we train a model to predict whether a credit card transaction is Fraudulent or Not. After training the model, we apply it to a test set of 200 new transactions (also labelled) and the model produces the contingency table below. Predicted Class Fraud Not Fraud True Fraud 60 Class Not Fraud 120 20 st your crisp point-wise observations on the classifier with supporting justification. (3 marks)
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TP True Positivecorrect prediction FP False Positiveincorrect prediction FN False Negativewhich are ... View the full answer
Related Book For
Statistics for Managers Using Microsoft Excel
ISBN: 978-0133130805
7th edition
Authors: David M. Levine, David F. Stephan, Kathryn A. Szabat
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