Growing and Pruning Ensembles in Python
Ensemble member selection refers to algorithms that optimize the composition of an ensemble.
This may involve growing an ensemble from available models or pruning members from a fully defined ensemble.
The goal is often to reduce the model or computational complexity of an ensemble with little or no effect on the performance of an ensemble, and in some cases find a combination of ensemble members that results in better performance than blindly using all contributing models directly.
In this tutorial, you will discover how to develop ensemble selection algorithms from scratch.
After completing this tutorial, you will know:
- Ensemble selection involves choosing a subset of ensemble members that results in lower complexity than using all members and sometimes better