Random Forest has _________ as base learning models1 pointmultiple decision treesbaggingentropyNone of these

Which of the following machine learning algorithm is based upon the idea of bagging?(1 Point)Decision treeRandom-forestClassificationRegression

This is an ensemble model that does not use bootstrapped samples to fit the base trees, takes residuals into account, and fits the base trees iteratively:1 pointBoostingRandom TreesBaggingRandom Forest

Which of the following are true about bagging?Answer choicesSelect only one optionREVISITIn bagging, we choose random subsamples of the data points with replacement.In bagging, individual trees are independent of each other.Bagging helps to reduce variance, and by extension, prevents overfitting.

Order these tree ensembles in order of most randomness to least randomness:1 pointRandom Trees, Random Forest, BaggingRandom Forest, Bagging, Random TreesRandom Forest, Random Trees, BaggingBagging, Random Forest, Random Trees

Which of the following algorithms is not an example of ensemble learning algorithm?2 pointsRandom ForestAdaboostExtra TreesGradient BoostingDecision Trees