Anomaly Detection Cheat Sheet

Explains point, contextual, and collective anomalies, key detection algorithms like One-Class SVM and autoencoders, and how to evaluate results on imbalanced data.

2 PagesIntermediateMar 2, 2026

Types of Anomalies

Categorize the anomaly before picking a technique.

Point anomaly- A single data instance that is far from the rest of the data (e.g., one huge transaction)
Contextual anomaly- Normal in general but abnormal given a specific context (e.g., high heating usage in summer)
Collective anomaly- A group of related instances that is anomalous together, even if individual points look normal
Global vs. local- Global anomalies deviate from the whole dataset; local anomalies deviate only from their neighborhood

One-Class SVM

Learn a boundary around normal data using only non-anomalous training examples.

python

from sklearn.svm import OneClassSVM# Train only on data assumed to be normalclf = OneClassSVM(kernel='rbf', nu=0.05, gamma='scale')clf.fit(X_train_normal)preds = clf.predict(X_test)   # 1 = normal, -1 = anomaly

Autoencoder Reconstruction Error

Flag inputs the network reconstructs poorly as anomalies.

python

import torch, torch.nn as nnclass Autoencoder(nn.Module):    def __init__(self, in_dim, latent_dim=8):        super().__init__()        self.encoder = nn.Sequential(nn.Linear(in_dim, 32), nn.ReLU(), nn.Linear(32, latent_dim))        self.decoder = nn.Sequential(nn.Linear(latent_dim, 32), nn.ReLU(), nn.Linear(32, in_dim))    def forward(self, x):        return self.decoder(self.encoder(x))# After training on normal data only:recon = model(x_batch)error = torch.mean((recon - x_batch) ** 2, dim=1)anomalies = error > threshold   # threshold set from validation error distribution

Evaluating Anomaly Detectors

Anomaly datasets are almost always heavily imbalanced.

Precision@k- Fraction of the top-k flagged points that are true anomalies; useful when review capacity is limited
Recall- Fraction of true anomalies that were caught; often prioritized when missed anomalies are costly
PR-AUC- Area under the precision-recall curve; more informative than ROC-AUC on rare-class problems
F1 / F-beta score- Harmonic mean of precision and recall; use F-beta to weight recall higher when misses are costly

Pro Tip

Never tune the anomaly threshold on the test set -- pick it from a held-out validation set's score distribution, then evaluate once on test to get an honest estimate of production performance.

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