• No products in the cart.

204.7.3 Types of Ensemble Models

popular ensemble methodologies

Link to the previous post : https://statinfer.com/204-7-2-ensemble-models/

In this short post we will just see the types of Ensemble models.

Types of Ensemble Models

  • The above example is a very primitive type of ensemble model. There are better and statistically stronger ensemble methods that will yield better results.
  • Two most popular ensemble methodologies are
    • Bagging
    • Boosting


  • Take multiple boot strap samples from the population and build classifiers on each of the samples. For prediction take mean or mode of all the individual model predictions.
  • Bagging has two major parts 1) Boot strap sampling 2) Aggregation of learners.
  • Bagging = Bootstrap Aggregating
  • In Bagging we combine many unstable models to produce a stable model. Hence, the predictors will be very reliable(less variance in the final model).

Boot strapping

  • We have a training data is of size N.
  • Draw random sample with replacement of size N – This gives a new dataset, it might have repeated observations, some observations might not have even appeared once.
  • We are selecting records one-at-a-time, returning each selected record back in the population, giving it a chance to be selected again.
  • Create B such new datasets. These are called boot strap datasets.
The next post is about  the bagging algorithm.
Link to the next post : https://statinfer.com/204-7-4-the-bagging-algorithm/
0 responses on "204.7.3 Types of Ensemble Models"

Leave a Message


Statinfer derived from Statistical inference is a company that focuses on the data science training and R&D.We offer training on Machine Learning, Deep Learning and Artificial Intelligence using tools like R, Python and TensorFlow

Contact Us

We Accept

Our Social Links

How to Become a Data Scientist?

© 2020. All Rights Reserved.