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For example, suppose that we wanted our 1000 fraudulent records to represent 25% of the balanced training data set rather than the 1% represented by these records in the raw training data set That would mean that we could retain only 3000 nonfraudulent records We would then need to discard from the analysis 96,000 of the 99,000 nonfraudulent records, using random selection Of course, one always balks at discarding data, but in the data mining world, data are abundant Also, in most practical applications, the imbalance is not as severe as this 99-to-1 fraud example, so that relatively fewer records need be omitted Another bene t of balancing the data is to provide the classi cation algorithms with a rich balance of records for each classi cation outcome, so that the algorithms have a chance to learn about all types of records, not just those with high target frequency. rdlc gs1 128 Packages matching GS1-128 - NuGet Gallery
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