Overview

Stacking is an ensemble machine learning technique that enhances predictive power by combining multiple models. It works by training a series of base models on the same dataset and then using their outputs as features for another model, known as the meta-model.

This method allows for the creation of highly accurate predictions by leveraging diverse base models that capture different aspects of the data. Stacking is widely used in competitions such as those hosted on Kaggle where accuracy is paramount.

Key aspects

In 2026, stacking will be increasingly utilized in enterprise settings to improve decision-making processes through more robust predictive analytics. Companies like Microsoft and Google are expected to integrate advanced stacking techniques into their cloud ML platforms.

The relevance of stacking lies in its ability to mitigate the limitations of individual models by combining them effectively, making it a key technique for tackling complex real-world problems where no single model can excel universally.