// Hacker Noon · 4 March 2026

332K Orders Later: How Ensemble ML Cut False Positives by 35%

A 25-day production experiment processed 332K orders to compare a single Isolation Forest model against a 3-model ensemble (Isolation Forest, LSTM, Autoencoder) for data quality monitoring. The ensemble reduced false positives by 35% and caught 30% more real anomalies, with only a slight increase in...

Hacker Noon

@hacker-noon · Pradeep Kalluri

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