| Table Of Confusion |
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''Table 1: Table of Confusion.'' For example, consider a model which predicts for 10,000 Insurance Claims whether each case is Fraudulent . This model correctly predicts 9,700 non-fraudulent cases, and 100 fraudulent cases. The model also incorrectly predicts 150 cases which are not fraudulent to be fraudulent, and 50 cases which are fraudulent to be non-fraudulent. The resulting Table of Confusion is shown below.
''Table 2: Example Table of Confusion.'' |
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