Stacking involves a multi-layered approach where multiple base models (often referred to as level-0 models) are first trained on the dataset. The predictions made by these base models are then used as inputs for a higher-level model (level-1 model), which combines these predictions to produce the final output. This higher-level model can be a linear model, a decision tree, or another advanced machine learning algorithm.
