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- Oversampling_and_undersampling_in_data_analysis abstract "Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).Oversampling and undersampling are opposite and roughly equivalent techniques. They both involve using a bias to select more samples from one class than from another.The usual reason for oversampling is to correct for a bias in the original dataset. One scenariowhere it is useful is when training a classifier using labelled training data from a biased source, sincelabelled training data is valuable but often comes from un-representative sources.For example, suppose we have a sample of 1000 people of which 66% are male (perhaps the sample was collectedat a football match). We know the general population is 50% female, and we may wish to adjust our dataset to represent this. Simple oversampling will select each female example twice, and this copying will produce a balanced dataset of 1333 samples with 50% female. Simple undersampling will drop some of the male samples at random to give a balanced dataset of 667 samples, again with 50% female.There are also more complex oversampling techniques, including the creationof artificial data points.".
- Oversampling_and_undersampling_in_data_analysis wikiPageExternalLink SPRINGER05.pdf.
- Oversampling_and_undersampling_in_data_analysis wikiPageExternalLink 307-K0020.pdf.
- Oversampling_and_undersampling_in_data_analysis wikiPageID "22101888".
- Oversampling_and_undersampling_in_data_analysis wikiPageRevisionID "603267611".
- Oversampling_and_undersampling_in_data_analysis date "April 2011".
- Oversampling_and_undersampling_in_data_analysis hasPhotoCollection Oversampling_and_undersampling_in_data_analysis.
- Oversampling_and_undersampling_in_data_analysis reason "say why this is a good thing as throwing away data clearly loses information".
- Oversampling_and_undersampling_in_data_analysis subject Category:Data_analysis.
- Oversampling_and_undersampling_in_data_analysis subject Category:Lean_manufacturing.
- Oversampling_and_undersampling_in_data_analysis comment "Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).Oversampling and undersampling are opposite and roughly equivalent techniques. They both involve using a bias to select more samples from one class than from another.The usual reason for oversampling is to correct for a bias in the original dataset.".
- Oversampling_and_undersampling_in_data_analysis label "Oversampling and undersampling in data analysis".
- Oversampling_and_undersampling_in_data_analysis sameAs m.05n_nz0.
- Oversampling_and_undersampling_in_data_analysis sameAs Q7113891.
- Oversampling_and_undersampling_in_data_analysis sameAs Q7113891.
- Oversampling_and_undersampling_in_data_analysis wasDerivedFrom Oversampling_and_undersampling_in_data_analysis?oldid=603267611.
- Oversampling_and_undersampling_in_data_analysis isPrimaryTopicOf Oversampling_and_undersampling_in_data_analysis.