Which of the following is/are true about boosting trees? 1) In boosting trees, individual weak learners are independent of each other 2) It is the method for improving the performance by aggregating the results of weak learnersReview Later12All of the AboveNone of the Above

Which of the following is true about the Gradient Boosting trees?2 pointsIn each stage, introduce a new regression tree to compensate the shortcomings of existing modelWe can use gradient decent method for minimize the loss functionNeither A nor BBoth A and B

Which of the following statements is not true about the Decision tree?1 pointa) It starts with a tree with a single leaf and assign this leaf a label according to a majority vote among all labels over the training setb) It performs a series of iterations and on each iteration, it examine the effect of splitting a single leafc) It defines some gain measure that quantifies the improvement due to the splitd) Among all possible splits, it either choose the one that minimizes the gain and perform it, or choose not to split the leaf at all

Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods?3 pointsBoth methods can be used for classification taskRandom Forest is use for classification whereas Gradient Boosting is use for regression taskRandom Forest is use for regression whereas Gradient Boosting is use for Classification taskBoth methods can be used for regression task

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

nce of boosting.svm = ' find the ideal line in the training data and than seperate to different group'knn = 'find the cloest data point of training data 'naivebayes = 'calculate the probability of training data and based on it make the prediction'decisiontree = 'used if-than-else rule to make prediction or decision, the model like a tree'right?