Core ML Concepts · intermediate · concept 34 of 311 · Updated Nov 4, 2026

🎻Ensemble Learning

Ensemble learning combines several models so that their individual errors cancel out, usually beating any single one. Bagging trains models independently and averages them, as random forests do; boosting trains them in sequence, each fixing the last one's mistakes. For tabular data these methods still often beat deep learning, at lower cost and with less tuning.