Outliers, or outliers, are values that deviate significantly from the majority of other values in a dataset. In the context of artificial intelligence and machine learning, outliers can be a major problem because they can have a negative effect on the accuracy and effectiveness of machine learning models. Outliers can be the result of measurement errors, data entry errors or rare and infrequent events in the dataset environment. If not handled properly, outliers can bias machine learning models and generate inaccurate or insufficient predictions. Therefore, it is important to identify and deal with outliers in datasets before using them to train machine learning models. Common methods for handling outliers include removing outliers, transforming the data to reduce their impact, and using robust models that are less sensitive to outliers.
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