Predictive Model Ensembles: Pros and Cons
Perficient Data & Analytics
NOVEMBER 7, 2019
Many recent machine learning challenges winners are predictive model ensembles. Pros of Model Ensembles. We should choose the best model from a collection of choices. Generally, ensembles have higher predictive accuracy. Test results improve with the size of the ensemble. Tweaking makes models fit better. With a bagging approach, each model should be tuned to overfit. Predictions can be softened for improved stability.