“Unity is strength”. This old saying expresses pretty well the underlying idea that rules the very powerful “ensemble methods” . Ensemble methods* are techniques that combine the decisions from several base machine learning (ML) models to find a predictive model to achieve optimum results. This algorithm can be any machine learning algorithm such as logistic regression, decision tree, etc. These models, when used as inputs of ensemble methods, are called ‘base-models”.
Consider the fable of the blind men and the elephant depicted in the image below. The blind men are each describing an elephant from their own point of view. Their descriptions are all correct but incomplete. Their understanding of the elephant would be more accurate and realistic if they came together to discuss and combine their descriptions.
With these examples, you can infer that a diverse group of people are likely to make better decisions as compared to individuals. Similar is true for a diverse set of models in comparison to single models. This diversification in Machine Learning is achieved by a technique called Ensemble Learning.
Ensemble machine learning can be mainly categorized into: Parallel ensemble, popularly known as bagging and Sequential ensemble, popularly known as boosting.