Ensemble Learning: Stacking, Blending & Voting


We have heard the phrase "unity is strength", whose meaning can be transferred to different areas of life. Sometimes correct answers to a specific problem are supported by several sources and not just one. This is what Ensemble Learning tries to do, that is, to put together a group of ML models to improve solutions to specific problems. Throughout this blog, we will learrn what Ensemble Learning is, what are the types of Ensembles that exist and we will specifically address Voting and Stacking Ensembles. Ensemble Learning refers to the use of ML algorithms jointly to solve classification and/or regression problems mainly. These algorithms can be the same type (homogeneous Ensemble Learning) or different types (heterogeneous Ensemble Learning).

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