Overview
Outliers are data points that deviate significantly from the rest of the dataset. They can be caused by measurement errors, experimental anomalies, or they might represent genuine but rare events.
Detection Methods
- Z-Score: Identifying points that are many standard deviations from the mean.
- Interquartile Range (IQR): Identifying points that fall far outside the 'box' in a box plot.
- DBSCAN: Identifying points in low-density regions.
- Isolation Forest: An algorithm specifically designed for anomaly detection.
Handling Outliers
Outliers can be removed, transformed, or kept, depending on whether they are considered errors or valuable insights.