How to Develop a Weighted Average Ensemble With Python
Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution when making predictions.
The weighted average or weighted sum ensemble is an extension over voting ensembles that assume all models are equally skillful and make the same proportional contribution to predictions made by the ensemble.
Each model is assigned a fixed weight that is multiplied by the prediction made by the model and used in the sum or average prediction calculation. The challenge of this type of ensemble is how to calculate, assign, or search for model weights that result in performance that is better than any contributing model and an ensemble that uses equal model weights.
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