Outlier Detection and Analysis Methods

Outlier detection is a crucial aspect of machine learning that involves identifying anomalous data points that can skew trends and affect the accuracy of models. Machine learning models, which depend on large datasets for training, require continuous monitoring for outliers to ensure data quality and model effectiveness. Outliers can arise from various errors in data collection, processing, or as natural anomalies, and are categorized into point, contextual, and collective outliers. Techniques for detecting outliers include using distance and density metrics, as well as predictive modeling of data point distributions. Seldon provides a comprehensive framework for outlier detection, offering tools like Alibi Detect, which includes algorithms such as Mahalanobis Distance, Isolation Forest, Variational Auto-Encoder, and Sequence to Sequence. These tools support different data types and use cases, enhancing model accuracy and reliability. Seldon emphasizes flexibility, standardization, and real-time monitoring to transform complex machine learning deployments into strategic advantages for businesses.