Editing Anomaly Detection (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Anomaly''' — A data point or pattern that deviates significantly from the expected distribution; also called outlier or novelty.
* '''Outlier''' — A data point that lies far from the majority of the data distribution.
* '''Novelty detection''' — Detecting new types of data not seen during training; the training data is assumed clean (normal).
* '''Outlier detection''' — Identifying anomalies when the training data itself may contain outliers (contaminated training).
* '''Point anomaly''' — A single data instance that is anomalous with respect to the rest of the data.
* '''Contextual anomaly''' — A data point that is anomalous only in a specific context (e.g., temperature of 30°C is normal in summer but anomalous in winter).
* '''Collective anomaly''' — A collection of data points that is collectively anomalous even though individual points may not be.
* '''Isolation Forest''' — An anomaly detection algorithm that isolates anomalies by randomly partitioning feature space; anomalies are isolated quickly.
* '''One-Class SVM''' — A support vector machine trained on normal data only, learning a boundary around it.
* '''Autoencoder for anomaly detection''' — A neural network trained to reconstruct normal data; anomalies have high reconstruction error.
* '''Local Outlier Factor (LOF)''' — Measures the local density of each point relative to its neighbors; anomalies have lower local density.
* '''DBSCAN''' — A clustering algorithm that identifies noise points (potential anomalies) as points not belonging to any cluster.
* '''Reconstruction error''' — In autoencoder-based detection, the error between input and reconstruction; high error indicates anomaly.
* '''Threshold''' — The score above which a data point is flagged as anomalous; setting this is a key tuning challenge.
* '''False positive rate (FPR)''' — Fraction of normal points incorrectly flagged as anomalous; must be kept low for operator usability.
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