Soft capping in machine learning, also known as Winsorization, involves setting a threshold on extreme values of a dataset to handle outliers effectively[1][4]. This technique includes flooring lower values and capping higher values based on specific percentiles to improve the accuracy and performance of predictive models[2]. By addressing irregular outliers without discarding them, soft capping helps in managing skewed data distributions and enhancing the overall impact of machine learning models[3].
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