Ensemble Models and When to Use Them

Ensembles are sophisticated machine learning models that comprise of other simpler models. They are quite useful when accuracy is the fundamental requirement, and computational resources are not a severe limitation. Ensembles are trendy in all sorts of current projects as they have significant advantages over conventional models. Let's take a closer look at this through this not-too-technical article.

Ensembles are an essential part of modern data science as they are more robust and powerful as models. Additionally, ensembles are ideal in cases of complex problems (something increasingly common in data science), as they can provide better generalization and more stability. In cases where conventional models fail to provide decent results, ensembles tend to work well enough to justify using data science in the problem at hand. That's why data scientists usually use them after all attempts to solve the problem with conventional models have failed.

The most common kind of ensembles is those based on the decision tree model. The main reason for this is because this kind of model is relatively fast to build and train, while it yields reasonably good performance. What's more, it's easy to interpret, something that bleeds to the ensemble itself, making it a relatively transparent model. Other ensembles are based on the combination of different models, belonging to different families. This heterogeneous architecture in the ensemble enables it to be more diverse in processing the data and to yield better performance. For all the ensembles out there, there needs to be a heuristic in place to figure out how the different outputs of the models that comprise the ensemble are fused. The most straightforward such heuristic is the majority voting, which works quite well in cases when all the ensemble models are similar.

The main drawback of ensembles is a particular comprise you have to make when using them. Namely, the transparency greatly diminishes, while in some cases, it disappears altogether. This phenomenon can be an issue when you need the model you built to be somewhat interpretable. Additionally, ensembles can overfit if the dataset isn't large enough or if they comprise of a large number of models. That's why they require special care and are better off being handled by experienced data scientists. Finally, ensembles require more computational resources than most data models. As a result, you need to have a good reason for using them in a large-scale scenario, since they can be quite costly.

You can learn more about ensembles and other machine learning topics in one of my recent books. Namely, in the Julia for Machine Learning book, published last year, I extensively cover ensembles and several other relative topics. Using Julia as the language through which the various machine learning models and methods are implemented, I examine how all this can be useful in handling a data science project robustly. Check it out when you have a moment. Cheers!