Ensembling

Combine Model Predictions with Ensembling

A machine learning model can fail because of patterns specific to its training run: its initialization, sampled batches, training data, architecture, or hyperparameters. Training another model may produce different mistakes. Ensembling uses that disagreement by combining predictions from multiple models instead of trusting one model alone. The idea is simple, but useful ensembles require more than averaging everything available. Models that make nearly identical errors provide little complementary information, while diverse models can improve predictions at the cost of additional training, memory, and inference work.