Outliers

In data science, outliers are data points that significantly differ from other observations in a dataset. Outliers can occur due to measurement errors, data corruption, or rare events. Outliers can have a significant impact on statistical analysis, as they can skew the results and affect the accuracy of predictive models. Therefore, it is important to identify and handle outliers appropriately. Outlier detection techniques include statistical methods such as z-score, modified z-score, and box plots, as well as machine learning algorithms such as clustering and anomaly detection. Outliers can be handled by removing them from the dataset, transforming them, or treating them as a separate class in predictive models.
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