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This topic has appeared in the trending rankings 1 time(s) in the past year. While it does not trend frequently, its appearance suggests a renewed or concentrated surge of public interest.
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Synthetic_minority_oversampling_technique entered the ranking for the first time today at position #. This is its highest position ever recorded.
This topic has appeared in the English Wikipedia rankings 1 time. It first appeared on 2026-05-23 and was most recently seen on 2026-05-23.
In statistics, synthetic minority oversampling technique (SMOTE) is a method for oversampling samples when dealing with imbalanced classification categories within a dataset. The problem with doing statistical inference and modelling on imbalanced datasets is that the inferences and results from those analyses will be biased towards the majority class. Other solutions undersample the majority class to be equivalently represented in the data with the minority class. Instead of undersampling the majority class, SMOTE oversamples the minority class. However, this technique has been shown to yield poorly calibrated models, with an overestimated probability to belong to the minority class.
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