Capco Technologies Accounts Manager Interview Questions and Answers

Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.

• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

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Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.

• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.