What is gradient boosting vs Random Forest?

Like random forests, gradient boosting is a set of decision trees. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time.

Which of the following is are true about Random Forest and gradient boosting ensemble methods?

Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods? Both algorithms are design for classification as well as regression task. Random forest is based on bagging concept, that consider faction of sample and faction of feature for building the individual trees.

What is the relation between Random Forest and ensemble learning?

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees.

When you use the boosting algorithm you always consider the weak learners?

26) When you use the boosting algorithm you always consider the weak learners. Which of the following is the main reason for having weak learners? To prevent overfitting, since the complexity of the overall learner increases at each step.

Which of the following are true about an individual TK tree in random forest?

Which of the following is true about individual(Tk) tree in Random Forest? Random forest is based on bagging concept, that consider faction of sample and faction of feature for building the individual trees.

Does random forest use learning rate?

Random Forest and Extra Trees don’t have learning rate as a hyperparameter.

Is gradient boosting ensemble learning?

The Gradient Boosting Machine is a powerful ensemble machine learning algorithm that uses decision trees. Boosting is a general ensemble technique that involves sequentially adding models to the ensemble where subsequent models correct the performance of prior models.