machine learning improving performance of ml models performance improvement with ensembles ensembles can give us boost in the machine learning result by combining several models basicallyensemble models consist of several individually trained supervised learning models and their results are merged in various ways to achieve better predictive performance compared to single model ensemble methods can be divided into following two groupssequential ensemble methods as the name impliesin these kind of ensemble methodsthe base learners are generated sequentially the motivation of such methods is to exploit the dependency among base learners parallel ensemble methods as the name impliesin these kind of ensemble methodsthe base learners are generated in parallel the motivation of such methods is to exploit the independence among base learners ensemble learning methods the following are the most popular ensemble learning methods the methods for combining the predictions from different modelsbagging the term bagging is also known as bootstrap aggregation in bagging methodsensemble model tries to improve prediction accuracy and decrease model variance by combining predictions of individual models trained over randomly generated training samples the final prediction of ensemble model will be given by calculating the average of all predictions from the individual estimators one of the best examples of bagging methods are random forests boosting in boosting methodthe main principle of building ensemble model is to build it incrementally by training each base model estimator sequentially as the name suggestsit basically combine several week base learnerstrained sequentially over multiple iterations of training datato build powerful ensemble during the training of week base learnershigher weights are assigned to those learners which were misclassified earlier the example of boosting method is adaboost