How to Develop a Weighted Average Ensemble for Deep Learning Neural Networks

A modeling averaging ensemble combines the prediction from each model equally and often results in better performance on average than a given single model. Sometimes there are very good models that we wish to contribute more to an ensemble prediction, and perhaps less skillful models that may be useful but should contribute less to an ensemble prediction. A weighted average ensemble is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance. In this tutorial, you will discover how to develop a weighted average ensemble of deep learning neural network models in Python with Keras. How to Develop a Weighted Average Ensemble for Deep Learning Neural Networks Photo by Simon Matzinger, some rights reserved. Model averaging is an approach to ensemble learning where each ensemble member contributes an equal amount to the final prediction. In the case of regression, the ensemble prediction is calculated as the average of the member predictions.