Statistical Modeling: Ensembles: How Models Vote for a Final Output
What are Ensembles Ensemble methods in predictive modeling are like gathering the opinions of multiple experts to make a final decision. Instead of relying on just one model to predict future market trends or ETF performance, ensemble techniques combine several different models to improve accuracy and reduce the risk of errors. Using a group of models, instead of just one, Ensembles "vote" on what should be the final output, leading to more reliable and balanced predictions. In regression models, ensemble methods typically use averaging to arrive at a final prediction. The final prediction is the average of all the individual predictions from these models. In boosting techniques, models are built sequentially, each improving upon the errors of the previous ones, and their predictions are also averaged to yield a final result. By combining multiple models, ensemble methods smooth out any individual errors, leading to more accurate and reliable predictions in tasks such a...