Ensembles
- An ensemble takes multiple individual learning models and combines them to produce an aggregate model that is more powerful than any of its individual learning models alone
- Each individual model might overfit to a different part of the data
- By combining different individual models into an ensemble, we can average out their individual mistakes to reduce the risk of overfitting while maintaining strong prediction performance.
Random forests are an example of the ensemble idea applied to decision trees.
random forest method that builds and combines a forest of randomly different trees in parallel