Anomalies vs Outliers Anomaly detection, or finding needles in a haystack, is an important tool in data exploration and unsupervised analytic modeling. Anomaly detection also creates a path to supervised modeling by singling out key examples that an analyst can begin to classify as needles or hay. Those labeled examples are essential for supervised learning, which is much more powerful than unsupervised learning methods like clustering. Though anomaly and outlier are often used interchangeably we’d like to emphasize distinct definitions. As Ravi Parikh describes well in a blog post[1], “An outlier is a legitimate data point that’s far
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