An outlier in machine learning is a data point that differs significantly from other observations in the dataset. Outliers can be caused by measurement or input errors, or they can be legitimate but rare instances.
Handling Outliers in Machine Learning
Identifying and handling outliers is crucial as they can skew results and affect the performance of machine learning models. Techniques for dealing with outliers include data transformation, binning, and using robust algorithms less sensitive to outliers.
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