What is blending in ensemble learning?

Blending is a simplified stacking technique that uses a holdout validation set instead of cross-validation.

Blending trains base models on training data and uses a separate holdout set to train the meta-model. Unlike stacking, which uses cross-validated predictions, blending is simpler but less efficient in data usage. It is faster but may introduce bias due to limited holdout data.