Which is fine in the beginning when you show predictive accuracy, but blows up in your face when you cannot explain the model anymore.įist, I will show you a bit how the data looks like. It is about not knowing what it is that you did in the first place. As such, this post is mostly about creating ensembles, but also about the danger of creating them which is more than just overfitting. It also make the promise of machine learning laughable. I want the models to appear in the box-plots with ascending order based on the year i.e. The models are named by the years they refer to: 2000, 2001, 2002. To briefly put this in context, I have been working through some exercises in Max Kuhn and Kjell Johnsons Applied.
Perhaps the biggest problem is how easy it is to to create these models, especially with available software nowadays. I am plotting box-plots of the accuracy scores of resamples of yearly models trained with caret. However, these chimera models are almost impossible to explain as they literally are made up of a combination of other models. Some features (like ntrees) will usually continue to reduce overall error as. Combined models are often referred to as ensemble models and there. Caret works by finding the parameter value where we have the lowest overall error.
Caret models how to#
By combining models you hope that each model adds something that the other model did not see, thereby improving predictive accuracy. In this post I will show you how to easily combine models using the caret package in R. The biggest advantage of ensembles is also its biggest limitation and that is its power. Combined models are often referred to as ensemble models and there are many ways of building and entwining ensembles. In this post I will show you how to easily combine models using the caret package in R.
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