Auto Topic: fraudulent
auto_fraudulent | topic
Coverage Score
1
Mentioned Chunks
2
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
fraudulent
Relations
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Evidence Chunks
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textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | go beyond that, a classifier will have to pay more attention to the fraudulent examples. To help it do that, you can undersample the majority Undersampling class (i.e., ignore some of the “valid” class examples) or over-sample the minority class (i.e., Over-sample duplicate some o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... asses. For example, Unbalanced classes a training set of credit card transactions might consist of 10,000,000 valid transactions and 1,000 fraudulent ones. A classifier that says “valid” regardless of the input will achieve 99.99% accuracy on this data set. To go beyond that, a cl ... |