Stacking is an advanced ensemble learning technique in machine learning that combines multiple predictive models to improve overall performance. By leveraging the strengths of various base models, stacking creates a “super-model” that can yield more accurate predictions than any individual model alone. This method involves training several base models on the same dataset and then using their predictions as input for a higher-level model, often referred to as a meta-model. Stacking is particularly effective in complex problems where diverse models can capture different patterns in the data, ultimately enhancing predictive accuracy and robustness.