remote sensing is the best for you!

Ensemble refers to using many to form one,  remote sensing similar to how one would gather information from many sources and aggregate their responses to come up with a wiser decision  in Machine Learning use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Ensemble methods can be divided into Averaging methods are based on averaging the predictions of several estimators while boosting methods have base estimators that are built sequentially and are to reduce the bias of the combined estimator. The commonly used classes of the two methods are forests of randomized trees,  bagging, stacking, and gradient tree boosting. Bagging involves building a predictor on many random subsets of the same dataset and averaging the predictions. It thus reduces the variance of the base estimator.  On the other hand, stacking involves fitting many different model types on the same data and using another model to learn how to best combine the predictions. Boosting involves adding ensemble members sequentially that correct the predictions made by prior models and output a weighted average of the predictions.