Outlier Detection
Outlier detection is the process of identifying data points that deviate significantly from other observations in a dataset. In data science, outlier detection is used to identify anomalies that may be indicative of errors in data collection or measurement, or may represent important but previously unknown phenomena. Outlier detection can be performed using a variety of statistical and machine learning techniques, including clustering, density estimation, and distance-based methods. Outlier detection is an important step in data preprocessing and can be used to improve the accuracy of predictive models by removing or down-weighting anomalous observations.
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