Simply speaking, ensemble machine learning refers to a technique that integrates output from multiple learners and is applied to a dataset to make a prediction. These multiple learners are usually referred to as base learners. When multiple base models are used to extract predictions that are combined into one single prediction, that prediction is likely to provide better accuracy than individual base learners.
Ensemble models are known for providing an advantage over single models in terms of performance. They can be applied to both regression and classification problems. You can either decide to build ensemble models with algorithms from the same family or opt to pick them from different families. If multiple models are built on the same dataset using neural networks only, then that ensemble would be called a homogeneous ensemble model...
