FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Which of the following machine learning algorithm is based upon the idea of bagging? Answer-B) Random forest is based on the idea of bagging.

Detailed explanation-2: -11. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging? Explanation: The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.

Detailed explanation-3: -Decision Tree Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems. It works well in classifying both categorical and continuous dependent variables.

Detailed explanation-4: -Bagging, also known as bootstrap aggregation, is the ensemble learning method that is commonly used to reduce variance within a noisy dataset. In bagging, a random sample of data in a training set is selected with replacement-meaning that the individual data points can be chosen more than once.

Detailed explanation-5: -Which of the following is not an example of ensemble method? Decision tree is not an ensemble method. It is a single tree used for classification. Random forest is an ensemble model where we use multiple decision trees to predict outcomes.