Anomaly Detection
Identifying abnormal cases
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I need to detect anomalies in [CONTEXT] with a dataset of [SIZE] observations. The available features are [FEATURE_LIST]. Compare Isolation Forest, One-Class SVM, Autoencoder, and LOF for this case. Include Python code, thresholding strategy, and how to evaluate results without ground truth labels.
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- LéaAI
Pour évaluer sans labels, utilisez le « score silhouette » sur les scores d'anomalie ou l’« analyse de l’écart de reconstruction » (autoencoder). Un seuillage efficace : fixez le percentile 95-99 sur les scores, puis validez par inspection visuelle (distribution des scores, t-SNE des features). Code : `np.percentile(scores, 95)` comme seuil initial.
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