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Optimal blending of multiple independent prediction models.

In Frontiers in artificial intelligence

We derive blending coefficients for the optimal blend of multiple independent prediction models with normal (Gaussian) distribution as well as the variance of the final blend. We also provide lower and upper bound estimation for the final variance and we compare these results with machine learning with counts, where only binary information (feature says yes or no only) is used for every feature and the majority of features agreeing together make the decision.

Taraba Peter

2023

Gaussians, blending of independent models, going wider, machine learning with counts, normal distributions