Stacking

Stacking is a general term for placing items on top of one another, and in specialized contexts it denotes techniques that combine multiple elements to produce a result greater than the parts alone. It is used in fields such as data science, photography, logistics, and fitness, among others.

In machine learning and statistics, stacking (also called stacked generalization) refers to an ensemble method that trains multiple base models (level-0) to generate predictions, and then trains a meta-model (level-1) to blend those predictions into a final output. The meta-model learns how to weight and transform the base predictions. Cross-validation is often used to generate unbiased training data for the meta-model. Base models can be diverse, and common meta-models include linear or logistic regression.

In photography and microscopy, stacking combines several images to improve quality. Focus stacking aligns and merges images taken at different focal depths to increase apparent depth of field, while exposure stacking combines images with different exposures to extend dynamic range or reduce noise.

In warehousing and shipping, stacking describes the vertical arrangement of goods, often on pallets. The practice aims to maximize space and stability while respecting safety, weight, and handling constraints.

In fitness and nutrition, stacking refers to using multiple supplements or compounds together to enhance effects. The term is widely used in sports contexts and may also appear in discussions of performance-enhancing substances; safety, legality, and effectiveness vary by product and jurisdiction.

Across applications, stacking involves integrating components to achieve improved performance or efficiency, but it can also introduce risks such as overfitting, artifacts, instability, or interactions that require careful design and caution.