Stacking is an ensemble machine learning technique that combines multiple models to improve predictive performance. It involves training a new model to aggregate the predictions of several base models, which can lead to better accuracy than any single model alone. The process typically consists of two levels: the first level generates predictions from various base models, and the second level uses these predictions as input features to make the final prediction.

To implement stacking effectively, the data is usually split into training and validation datasets. The training data is used to build the stacking model, while the validation data is reserved for evaluating its performance. Various algorithms can be employed at the first level, such as logistic regression, random forests, and gradient boosting methods, which are then combined in the second level to produce the final output ²⁵.

Understanding when to use stacking is crucial, as it can enhance model performance in many scenarios, particularly in competitions like those on Kaggle, where accuracy is paramount ².