The Local Outlier Factor (LOF) is an algorithm used for anomaly detection in machine learning. It identifies anomalies or outliers in a dataset by comparing the local density of data points. The key idea behind LOF is that an outlier is a point that has a significantly lower density compared to its neighbors. ### Key Concepts of LOF: 1. **Local Density**: It measures how densely packed the points are around a given data point.
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