What Is Ensemble Learning ?

Ensemble learning is generally considered to be one of the most powerful ways of improving the performance of a model. In short, it is a process by which the models like classifiers, or the experts, are generated and combined together in order to solve a problem.

Ensemble learning is used for the process of improving the classification, prediction, and the function approximation performance of the model, reducing the likelihood of an unfortunate selection of the poor one.

The other applications of the Ensemble learning include assigning confidence to whatever decision is made by the machine learning model, selecting the optimal features, data fusion, non-stationary learning, incremental learning, and error correcting.

What is Ensemble learning?

An Ensemble learning system is generally a kind of process that helps in combining diverse models like the Classifiers. Therefore these systems are also called as the Multiple Classifiers system or just the Ensemble Base Systems. The ensemble methods can be divided into two major groups.

  • The first one is the sequential ensemble methods, where the base learner methods are generated sequentially, say, for example, AdaBoost. The main motive of the sequential methods is to exploit the level of dependency that is generated between the base learners. Their overall performance can then be boosted by weighing the mislabelled examples through a higher weight.
  • The second one is the parallel ensemble method, where the base learners are generated in the form of parallel, say, for example, the Random Forest. The main motivation of this method is to exploit the independence of the base learners since the errors that are caused by the models can be reduced by averaging.

Most of the ensemble methods are generally a single base algorithm that helps in producing homogeneous base learners, that is, the learners of the same type leading towards a homogenous ensemble.

Most Common Methods Learnt In Ensemble Learning


Bagging generally tries to implement the similar kind of learners on a smaller population and then goes ahead and takes the mean of all the predictions that are made. While generalizing the entire process of bagging you can actually go ahead and use different learners on a different kind of population. And as a result, the entire process helps in reducing variance errors.


Stacking is generally considered to be one of the most interesting methods of combining the models. Throughout the process of stacking, we use the learners to come forward and combine the output that is gained from the different learners. This will help in decreasing the bias or variance caused, depending on the combining learner that we use.


Boosting is a process that helps in adjusting the weight of the observation based on the last classification. In case, the observation made has been classified incorrectly, it then tries to increase the weight of the observation and vice versa. Boosting, in general, helps in decreasing the bias error that is caused and build stronger predictive models.

To Conclude

Ensemble techniques can be used and in fact, are used in the Kaggle problems. It is just that choosing the right ensembles is generally a kind of an art than a straightforward science. It is through experience, as you will have to develop a knack for ensembles learner in order to use it in the different scenarios and base learners.

We hope that this blog proved to be helpful for you with some useful information. So how would you go choosing the right ensemble technique? Do leave your thoughts and comments below. We would love to read the answers given by you.

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