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 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 given options is not associated with XGBoost?Select an option Clear ResponseIt has an in-built routine for the purpose of handling the missing values.It has lower predictive power than Gradient Boosting.It helps in reducing overfit.It implements parallel processing technique.

Supervised Learning Algorithms:3. Ensembling:o Combines the predictions of multiple machine learning models thatare individually weak to produce a more accurate prediction on a newsample.o Examples of ensemble techniques:• Bagging with Random Forests,• Boosting with XGBoost143. Ensembling:14监督学习算法:o结合个别弱的多个机器学习模型的预测，以对新样本产生更准确的预测。o集成技术的例子:•用随机森林装袋,•使用XGBoost提升

True or false: Traditionally, XGBoost is slower than lightGBM but it achieves faster training through the Histogram binning process.1 pointTrueFalse

16. A data analyst has trained an Extreme Gradient Boosting Algorithm using the default hyperparameters. The model obtained an accuracy of 80 percent. The analyst was advised that if she does hyperparameter tuning, she can improve the perfomance of the model. Which among the following methods can the analyst use. - i. Bayesian search- ii. Grid search- iii. Randomized searchii. onlyiii. onlyii. and iii. onlyi., ii., and iii.i. and ii. onlyi. and iii. onlyi. only