“Two heads are better than one.”
In the field of machine learning, one of the most effective ways to improve the accuracy of predictive models is through ensemble learning.
Ensemble learning is a machine learning technique that combines multiple models to create a more accurate and robust model than any individual model could achieve on its own. It leverages multiple machine learning algorithms collectively to address classification or regression problems. This is done by training several models on the same/independent dataset and then aggregating their predictions to make a final decision. The algorithms used can either be of the same type (homogeneous ensemble learning) or different types (heterogeneous ensemble learning).
Ensemble learning offers numerous advantages. Here, we’ll examine its most compelling benefits and explain why it’s such a powerful tool.
There are different types of Ensemble Learning techniques which differ mainly by the type of models used (homogeneous or heterogeneous models), the data sampling (with or without replacement, k-fold, etc.) and the decision function (voting, average, meta model, etc). Therefore, Ensemble Learning techniques can be classified as: