Ensemble techniques can be broadly divided into two types:
Averaging method: This is the method in which several estimators are run independently and their predictions are averaged. This includes random forests and bagging methods.
Boosting method: This is the method in which weak learners are built sequentially using weighted distributions of the data based on the error rates.
Ensemble methods use multiple models to obtain better performance than any single constituent model. The aim is to not only build diverse and robust models, but also work within limitations, such as processing speed and return times. When working with large datasets and quick response times, this can be a significant developmental bottleneck. Troubleshooting and diagnostics are an important aspect of working with all machine learning models, but especially when we are dealing with models that may take days to run.
The types of machine learning ensembles that can be created are as diverse as the models...