What is random forest and how bagging and boosting differs?

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Accepted Answer

Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.

Random forest is a type of ensemble learning method used for classification and regression tasks.
It builds multiple decision trees during training and combines their predictions to improve accuracy and reduce overfitting.
Bagging (Bootstrap Aggregating) is a technique used in random forests where each tree is trained on a random subset of the training data.
Boosting is another technique used in random forests where each tree is trained based on the errors made by the previous trees.
Random forest is less prone to overfitting compared to individual decision trees.
Example: Random forest can be used in predicting customer churn in a telecom company by combining multiple decision trees trained on customer data.

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