La puntuación de anomalía es una métrica numérica utilizada en análisis de datos and aprendizaje automático to assess how different or unusual a particular data point is compared to the expected behavior of a dataset. This score is particularly important in fields such as fraud detection, network security, and fault detection, where identifying outliers can help prevent significant issues or losses.
El cálculo de una puntuación de anomalía generalmente implica métodos estadísticos or machine learning algorithms that analyze patterns within the data. For example, in a supervised learning context, a model may be trained on a labeled dataset containing both normal and anomalous instances. Once trained, the model can generate an anomaly score for new, unseen data points based on how closely they align with the patterns observed in the training data.
Las técnicas comunes para calcular las puntuaciones de anomalía incluyen:
- Métodos estadísticos: Techniques such as z-scores or modified z-scores can identify how far un punto de datos se desvía de la media de un conjunto de datos.
- Enfoques de aprendizaje automático: Algorithms like Isolation Forest, One-Class SVM, or Autoencoders can be employed to detect anomalies by learning the general estructura de los datos.
- Medidas de distancia: Metrics such as Euclidean distance or distancia de Mahalanobis pueden ayudar a cuantificar qué tan lejos está un punto de datos de una distribución de referencia.
Once calculated, the Anomaly Score can be used to set thresholds that determine whether a data point is considered normal or anomalous. This enables organizations to take timely action when unusual patterns are detected, enhancing their ability to respond to potential threats or operational inefficiencies.