In my previous blog, Ensembles and Regularization – Analytics Superheroes, I reviewed the many advantages of model ensembles including removing “noise” variables, generalizing better than single component models, and reducing sensitivity to outliers.
In this article I take a deeper dive into the attributes and applications of model ensembles, and explore potential downsides to provide context for when to use them.
This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here
