Develop a Bagging Ensemble with Different Data Transformations
Bootstrap aggregation, or bagging, is an ensemble where each model is trained on a different sample of the training dataset.
The idea of bagging can be generalized to other techniques for changing the training dataset and fitting the same model on each changed version of the data. One approach is to use data transforms that change the scale and probability distribution of input variables as the basis for the training of contributing members to a bagging-like ensemble. We can refer to this as data transform bagging or a data transform ensemble.
In this tutorial, you will discover how to develop a data transform ensemble.
After completing this tutorial, you will know:
- Data transforms can be used as the basis