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Optimal blending of multiple independent prediction models.


ABSTRACT: 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.

SUBMITTER: Taraba P 

PROVIDER: S-EPMC9998929 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Optimal blending of multiple independent prediction models.

Taraba Peter P  

Frontiers in artificial intelligence 20230224


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. ...[more]

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