Day 20 - Advanced Ensemble Techniques - Bagging

       By Jerin Lalichan 

Advanced Ensemble Techniques

Bagging

    The idea behind bagging is to combine the results of multiple models to get a generalized result. But, the model created from the same set of data gives the same result. So to solve this problem we use a technique called Bootstrapping. This is one of the few methods to solve this problem.

    Bootstrapping is a sampling technique in which we create subsets of observations from the original dataset, with replacement. The size of the subsets is the same as the size of the original set.

    The bagging (or Bootstrap Aggregating) technique uses these subsets (bags) to get a fair idea of the distribution (complete set). The size of subsets created for bagging may be less than the original set.

1. Multiple subsets are created from the original dataset, selecting observations with replacements.
   
   
2. A base model (weak model) is created on each of these subsets.
   
   
3. The models run in parallel and are independent of each other.
   
   
4. The final predictions are determined by combining the predictions from all the models

   

  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.

Stay Curious!  

By Jerin Lalichan