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

The statements "Both methods can be used for classification task" and "Both methods can be used for regression task" are true.

Random Forest and Gradient Boosting are both ensemble learning methods and can be used for both classification and regression tasks. They work by combining multiple algorithms to obtain better predictive performance than could be obtained from any of the constituent algorithms alone.

Random Forest operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Gradient Boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.

So, it's not correct to say that Random Forest is used only for classification or Gradient Boosting is used only for regression. Both methods can be used for both types of tasks.