How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras

Model averaging is an ensemble technique where multiple sub-models contribute equally to a combined prediction. Model averaging can be improved by weighting the contributions of each sub-model to the combined prediction by the expected performance of the submodel. 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. In this tutorial, you will discover how to develop a stacked generalization ensemble for deep learning neural networks. How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras Photo by David Law, some rights reserved. A model averaging ensemble combines the predictions from multiple trained models.