Huawei HCIA-AI Practice Exam 2025 – Complete Study Resource

Ensemble averaging in machine learning refers to the technique of combining multiple models to produce a single improved model. One significant outcome of this approach is the improved accuracy in predictions. When various models, each with different strengths and weaknesses, are combined, the ensemble can generally leverage the diverse perspectives of these models to make more accurate predictions than any individual model could achieve on its own.

The rationale behind this improvement lies in the principle of variance reduction. Different models may make different errors on the same input data. By averaging their predictions, the individual errors can potentially cancel each other out, leading to a more robust overall prediction. This helps in mitigating overfitting, as an ensemble can generalize better on unseen data compared to a single model.

In contrast to the notion of increased training time or reduced model complexity, ensemble averaging typically requires more computational resources both in training and inference due to the dependency on multiple models. While ensemble methods do increase the overall complexity of the model in terms of the number of components involved, their strength lies in their ability to enhance prediction accuracy, making ensemble averaging a powerful technique in the field of machine learning.