Ensemble/Voting Classification in Python with Scikit-Learn
Ensemble classification models can be powerful machine learning tools capable of achieving excellent performance and generalizing well to new, unseen datasets.
The value of an ensemble classifier is that, in joining together the predictions of multiple classifiers, it can correct for errors made by any individual classifier, leading to better accuracy overall. Let’s take a look at the different ensemble classification methods and see how these classifiers can be implemented in Scikit-Learn.
What are Ensemble Models in Machine Learning?
Ensemble models are an ensemble learning method that combines different algorithms together. In this sense, it is a meta-algorithm rather than an algorithm itself. Ensemble learning methods are valuable because they can improve the performance of a predictive model.
Ensemble learning methods work off of the idea that tying the predictions of multiple classifiers together will lead to better performance by either improving prediction accuracy or reducing aspects like bias and variance.
In general, an ensemble model falls into one of two categories: sequential approaches and parallel approaches.
A sequential ensemble model operates by having the base learners/models generated in sequence. Sequential ensemble methods