The ensemble is a method used in the machine learning algorithm. In this method, multiple models or ‘weak learners’ are trained to rectify the same problem and integrated to gain desired results. Weak models combined rightly give accurate models.
First, the base models are needed to set up an ensemble learning method that will be clustered afterward. In the Bagging and Boosting algorithms, a single base learning algorithm is used. The reason behind this is that we will have homogeneous weak learners at hand, which will be trained in different ways.
The ensemble model made this way will eventually be called a homogenous model. But the story doesn’t end here. There are some methods in which different types of base learning algorithms are also implied with heterogeneous weak learners making a ‘heterogeneous ensemble model.’ But in this blog, we will only deal with the former ensemble model and discuss the two most popular ensemble methods herewith.
- Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
- Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.