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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 66.7% 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. The virtues of this exercise rely on the agenda of the sampler or pollster. The oversampling or undersampling bias could be used to compensate adverse conditions to a certain gender or age group and/or a race, in order to design and implement a good public policy. But also can be used to rick or mislead public opinion to approve a certain program that could benefit special interests. So, it is important to be aware of the companies or sponsors of certain statistical sampling, in order to know if they are positively compensating the sampling or they are trying to push a specific agenda.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.
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• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLink Bias.
• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLink Category:Data_analysis.
• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLink Category:Lean_manufacturing.
• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLink Data_set.
• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLink Oversampling.
• Oversampling_and_undersampling_in_data_analysis wikiPageWikiLinkText "Oversampling and undersampling in data analysis".
• 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 wikiPageUsesTemplate Template:Clarify.
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• Oversampling_and_undersampling_in_data_analysis wikiPageUsesTemplate Template:Statistics-stub.
• 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 hypernym Techniques.
• Oversampling_and_undersampling_in_data_analysis type Article.
• Oversampling_and_undersampling_in_data_analysis type Weapon.
• Oversampling_and_undersampling_in_data_analysis type Article.
• Oversampling_and_undersampling_in_data_analysis type Concept.
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• 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".
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• Oversampling_and_undersampling_in_data_analysis sameAs Supraeșantionarea_și_subeșantionarea_în_analiza_datelor.
• Oversampling_and_undersampling_in_data_analysis sameAs Q7113891.
• Oversampling_and_undersampling_in_data_analysis sameAs Q7113891.
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• Oversampling_and_undersampling_in_data_analysis isPrimaryTopicOf Oversampling_and_undersampling_in_data_analysis.