What is Ensemble Learning?

Ensemble learning is the process by which multiple models, such as classifiers , are strategically generated and combined to solve a particular problem it improves the performance of the model.

Types of Ensemble learning are:

• Stacking

 This is a very interesting way of combining models. Here we use a learner to combine output from different learners. This can lead to decrease in either bias or variance error depending on the combining learner we use.

• Bagging

Bagging method helps you to implement similar learners on small sample populations. It helps you to make nearer predictions.

• Boosting

Boosting is an iterative method which allows you to adjust the weight of an observation depends upon the last classification. Boosting decreases the bias error and helps you to build strong predictive models.