What is stacking in machine learning and how does it work?
What is stacked generalization in machine learning?
- This can be extended further by training an entirely new model to learn how to best combine the contributions from each submodel. This approach is called stacked generalization, or stacking for short, and can result in better predictive performance than any single contributing model.
Is there an implementation of stacking in Python for deep learning?
- For an example of implementing stacking from scratch for deep learning, see the tutorial: The scikit-learn Python machine learning library provides an implementation of stacking for machine learning. It is available in version 0.22 of the library and higher.
What is the architecture of a stacking model?
- The architecture of a stacking model involves two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, referred to as a level-1 model. Level-0 Models (Base-Models): Models fit on the training data and whose predictions are compiled.
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