Novelty Detection with Local Outlier Factor (LOF) in Scikit Learn

Novelty detection is a technique used to identify data points that are significantly different from the majority of data points in a dataset. These data points are often referred to as outliers or novelties. There are several approaches to novelty detection, including one-class classification, density-based methods, distance-based methods, and clustering-based methods.

In this video, the Local Outlier Factor (LOF) algorithm is explained as a method for novelty detection. LOF is a density-based outlier detection method that calculates the local density of each sample in the dataset and identifies samples that have a significantly lower density than their neighbors. These samples are considered to be outliers or novelties. The video also includes a code example that demonstrates how to use the LOF algorithm for novelty detection in scikit-learn.

For more details, please go through - Novelty Detection with Local Outlier Factor (LOF) in Scikit Learn