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> ML_ALGORITHM // LOCAL-OUTLIER-FACTOR_v1.0

Local Outlier Factor (LOF)

Density-based unsupervised outlier detection method measuring the local deviation of a given data point with respect to its neighbors.

Density Outlier Detectionclassical-unsupervisedhigh-intrinsicsmall (<1k)
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Computational Complexity
Training Complexity:O(n^2) or O(n log n) with spatial index
Inference Complexity:O(n) for novelty mode
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)

Interpretability Assessment

LOF ratio directly indicates the factor by which the point is less dense than its local neighborhood.

Suitable Tasks & Supported Modalities

Suitable Tasks:
anomaly detection
Supported Modalities:
tabular

Implementing Libraries

scikit-learnscikit-learn Consortium / Inria · v1.5.2
View Spec
pyod

Foundational Literature

Common Pitfalls & Warnings
  • Choosing k smaller than cluster size leads to high false positive rates in dense clusters