Local Outlier Factor (LOF) For Anomaly Detection

Author(s): Amy @GrabNGoInfo

Originally published on Towards AI.

LOF for novelty and anomaly detection

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Local Outlier Factor (LOF) is an unsupervised model for outlier detection. It compares the local density of each data point with its neighbors and identifies the data points with a lower density as anomalies or outliers.

In this tutorial, we will talk about

What’s the difference between novelty detection and outlier detection?When to use novelty detection vs. outlier detection?How to use Local Outlier Factor (LOF) for novelty detection?How to use Local Outlier Factor (LOF) for anomaly or outlier detection?

Resources for this post:

Video for this tutorial on YouTubePython code… Read the full blog for free on Medium.

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