basicallythe idea is that you have more than one modeland they might be the same kind of model or it might be different kinds of modelsbut you run them allon your set of training dataand they all vote on the final result for whatever it is you're trying to predict and oftentimesyou'll find that this ensemble of different models produces better results than any single model could on its own good examplefrom few years agowas the netflix prize netflix ran contest where they offeredi think it was million dollarsto any researcher who could outperform their existing movie recommendation algorithm the ones that won were ensemble approacheswhere they actually ran multiple recommender algorithms at once and let them all vote on the final result soensemble learning can be very powerfulyet simple toolfor increasing the quality of your final results in machine learning let us now try to explore various types of ensemble learningbootstrap aggregating or baggingnowrandom forests use technique called baggingshort for bootstrap aggregating this means that we take random subsamples of our training data and feed them into different versions of the same model and let them all vote on the final result if you rememberrandom forests took many different decision trees that use different random sample of the training data to train onand then they all came together in the end to vote on final result that' bagging boostingboosting is an alternate modeland the idea here is that you start with modelbut each subsequent model boosts the attributes that address the areas that were misclassified by the previous model soyou run train/tests on modelyou figure out what are the attributes that it' basically getting wrongand then you boost those attributes in subsequent models in hopes that those subsequent models will pay more attention to themand get them right sothat' the general idea behind boosting you run modelfigure out its weak pointsamplify the focus on those weak points as you goand keep building more and more models that keep refining that modelbased on the weaknesses of the previous one bucket of modelsanother techniqueand this is what that netflix prize-winner didis called bucket of modelswhere you might have entirely different models that try to predict something maybe ' using -meansa decision treeand regression can run all three of those models together on set of training data and let them all vote on the final classification result when ' trying to predict something and maybe that would be better than using any one of those models in isolation