Ensemble Learning and RandomForests in R
Rsupports ensemble learning. Basically, ensemble learning is curating the multiple predictions and combining them to generate strong overall prediction to overcome the assumptions and challenges from each method such as nearest neighbor models, logistic regression, Bayesian method, classification decision trees, or discriminate analysis. For an instance, Random Forest predictions and simple linear model and a vector machine can be combined to derive a strong prediction outcome. As the diversity of models increases, the ensemble learning increases significantly. Even combining multiple models in similar nature does not provide the best performance on the ensemble learning, the diversity of the models is what provides the best prediction possible outcome with strong results. The ensemble learning models created from the combinations of multiple models surpass the performance of prediction outcomes from a single model.