Author: MLNerds

Anomaly detection is an important task with many applications – right from finding outliers in the data to avoid building bad models to applications such as fraud detection. One of the main challenges with anomaly detection is the lack of labeled data to build supervised classifier models. This video gives a brief overview of five…

Datasets often contain anomalies or outliers whose properties are different from those of the regular data points. Such outliers if not handled could lead to bad models which do not work even for the typical non-outlier points. How do we identify outliers in data? There are many techniques to identify outliers. the Z-score is one…