Stacking is an ensemble machine learning technique that combines multiple models to improve predictive performance. The core idea is to use a two-level model structure. In the first level, various base models (often referred to as Level 0 models) are trained on the training dataset. These models generate predictions that serve as new features for the second level model (Level 1), which makes the final predictions.

The process typically begins with splitting the dataset into training and validation sets. The training set is used to train the base models, while the validation set is reserved for evaluating the performance of the stacking model. This approach can enhance accuracy and is particularly effective in competitions like those on Kaggle, where it can lead to improved leaderboard results ²⁵.

To implement stacking, libraries such as Scikit-Learn can be utilized, allowing for the integration of various algorithms, including logistic regression, random forests, and gradient boosting methods ⁵.

If you have more specific questions about stacking or its applications, feel free to ask!