![]() ![]() Under-representation of one class in the outcome (dependent) variable.If we were designing a survey to gather data, we would survey 4 times as many females as males, so that in the final sample, both genders will be represented equally. Suppose only 20% of software engineers are women, i.e., males are 4 times as frequent as females. It is known women are under-represented considerably in a random sample of software engineers, which would be important when adjusting for other variables such as years employed and current level of seniority. Suppose, to address the question of gender discrimination, we have survey data on salaries within a particular field, e.g., computer software. Under-representation of a class in one or more important predictor variables.Data Imbalance can be of the following types: Motivation for oversampling and undersampling īoth oversampling and undersampling involve introducing a bias to select more samples from one class than from another, to compensate for an imbalance that is either already present in the data, or likely to develop if a purely random sample were taken. There are also more complex oversampling techniques, including the creation of artificial data points with algorithms like Synthetic minority oversampling technique. ![]() Oversampling and undersampling are opposite and roughly equivalent techniques. These terms are used both in statistical sampling, survey design methodology and in machine learning. the ratio between the different classes/categories represented). Within statistics, Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. ( Learn how and when to remove this template message) ( April 2011) ( Learn how and when to remove this template message) Please help to improve this article by introducing more precise citations. This article includes a list of general references, but it lacks sufficient corresponding inline citations. ![]()
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