What is Boosting in Machine Learning?

In this post, we will see a simple and intuitive explanation of Boosting algorithms: what they are, why they are so powerful, some of the different types, and how they are trained and used to make predictions. We will avoid all the heavy maths and go for a clear, simple, but in depth explanation that can be easily understood. However, additional material and resources will be left at the end of the post, in case you want to dive further into the topic. Traditionally, building a Machine Learning application consisted on taking a single learner, like a Logistic Regressor, a Decision Tree, Support Vector Machine, or an Artificial Neural Network, feeding it data, and teaching it to perform a certain task through this data. Then ensemble methods were born, which involve using many learners to enhance the performance of any single one of them individually.