Local Outlier Factor

Local Outlier Factor (LOF) is a method used to identify unusual data points in a dataset. It evaluates how isolated a point is by comparing its characteristics to those of its neighboring points. If a point has significantly fewer neighbors or is distant from its neighbors compared to others, LOF assigns it a high score, flagging it as an outlier. Essentially, LOF focuses on the local context around each point, making it effective at detecting anomalies that differ from their nearby data, even if they seem normal when viewed across the entire dataset.