Anomaly Detection in Machine Learning

Anomaly Detection, a common topic in machine learning, is a field dedicated to detecting unexpected or behavior-deviating trends and events in data sets, which if left unnoticed, can disrupt or skew the data as a whole. Typically used in heavy data domains such as finance, economics, statistics, and other technology sectors, anomaly detection is becoming more popular in the world to ensure the absolute perfection of data findings. While once not relied on due to the criticisms anomaly detection algorithms grew for having high false-positive rates, algorithms over the years have developed significantly and the field is becoming more popular than ever. 

Software engineers, data analysts, and statisticians, are several among many of the professions who in the future will rely on anomaly detection to ensure their findings are as accurate as possible. Using more advanced techniques such as multivariate anomaly detection, findings become even more reliable as outliers are signaled out in an even greater pace. For example, anomalies can be spotted in VIF tests to search for multicolinearity, ensuring that the data isn't skewed because many factors have the same backbone to rely on. On the other hand, logistic regressions can show exactly where outliers lie in comparison to the two variables they're tested against to ensure outliers that have nothing to do with our data are left out as well.

Anomaly detection is becoming more prominent in society and as such, machine learning algorithms and all of technology continue to advance each and every year for the benefit of data scientists everywhere.