An puntuación de valores atípicos is a statistical measure used to identify and quantify how significantly a data point deviates from the expected norm within a given dataset. Outliers are data points that differ dramatically from other observations and can indicate variability in the measurement, experimental errors, or a novel phenomenon that warrants further investigation.
In many analytical scenarios, outliers can skew results and lead to misleading conclusions. Therefore, calculating an outlier score helps in making informed decisions about data cleaning and preprocessing. Common methods for determining outlier scores include técnicas estadísticas such as Z-scores, distancia de Mahalanobis, and various aprendizaje automático algorithms que evalúan la distancia o densidad de un punto en relación con el resto de los datos.
For instance, in a dataset where most values cluster around a mean, an outlier may have a high Z-score, indicating it is several standard deviations away from the mean. This score can help determine whether to exclude the outlier from analysis o investigar más a fondo su importancia.
Outlier scores are particularly useful in fields like finance for fraud detection, in healthcare for identifying anomalous patient data, and in machine learning for mejorar la robustez del modelo. The identification and treatment of outliers are crucial steps in ensuring the reliability and accuracy of data-driven insights.