Hybrids Constructed From Multiple Models

Hybrid models are those models that combine two or more models explicitly. As such, the definition of what does and does not constitute a hybrid model can be vague.

  • Hybrid Model : A technique that combines two or more different machine learning models in some way.

For example, an autoencoder neural network that learns how to compress input patterns to a bottleneck layer, the output of which is then fed to another model, such as a support vector machine, would be considered a hybrid machine learning model.

This example has two machine learning models, a neural network and a support vector machine. It just so happens that the models are linearly stacked one on top of another into a pipeline and the last model in the pipeline makes a prediction.

Consider an ensemble learning method that has multiple contributing ensemble members of different types (e.g. a logistic regression and a support vector machine) and uses voting to average their predictions. This ensemble too might be considered a hybrid machine learning model, under the broader definition.

Perhaps the key difference between ensemble learning and hybrid machine learning is the need in hybrid models to use models of differing types. Whereas in ensemble learning, contributing members to the ensemble may be of any type.

Further, it is more likely that a hybrid machine learning will graft one or more models onto another base model, which is quite different from fitting separate models and combining their predictions as we do in ensemble learning.