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 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

Gradient Boosting Framework: XGBoost operates within a gradient boosting framework, where a sequence of weak learners (decision trees) are trained iteratively to correct the errors made by the preceding models. This iterative process allows XGBoost to gradually improve its predictive accuracy by learning from the mistakes of earlier models.

Which of the following is a disadvantage of boosting algorithms?Review LaterBoosting can lead to underfittingBoosting can be computationally expensiveBoosting requires a large number of hyperparametersBoosting is not suitable for imbalanced data

What are the disadvantages of the decision tree?*1 point(A) Over-fitting of the data is possible.(C) We have to balance the dataset before training the model(B) The small variation in the input data can result in a different decision tree(D) All of the above

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