Ensemble Learning: Maximizing Predictive Power through Collective Intelligence

Author(s): David Andres

Originally published on Towards AI.

Photo by Cristina Marin on Unsplash

Ensemble Learning is a powerful method used in Machine Learning to improve model performance by combining multiple individual models. These individual models, also known as “base models” or “weak learners,” may have limitations such as high variance or high bias.

There are two main types of ensemble models:

Homogeneous Ensemble: Homogeneous ensembles combine multiple base models of the same type. These models are trained independently on different subsets of the training data and their predictions are combined to make the final prediction.Heterogeneous Ensemble: Heterogeneous ensembles combine base models of different types. These models may have various architectures… Read the full blog for free on Medium.

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Published via Towards AI