What ensemble model combines multiple decision trees to improve accuracy?

What ensemble model combines multiple decision trees to improve accuracy?

• A. Gradient boosting
• B. AdaBoost
• C. Random forest
• D. Bagging classifier

Answer: (C)

Random forest is an ensemble method that builds many decision trees and combines their predictions to improve accuracy. Each tree is trained on a bootstrap sample of the data, and at each split only a random subset of features is considered. This decorrelates the trees, so their errors don’t line up, and when their outputs are aggregated—by majority vote for classification or average for regression—the overall prediction tends to be more accurate and less prone to overfitting than a single decision tree. This is different from boosting methods like gradient boosting or AdaBoost, which build trees sequentially to correct the mistakes of previous ones. It’s also a more specific form of bagging that uses decision trees as the base estimators and adds feature randomness, rather than applying bootstrap aggregation to arbitrary models.

Random forest is an ensemble method that builds many decision trees and combines their predictions to improve accuracy. Each tree is trained on a bootstrap sample of the data, and at each split only a random subset of features is considered. This decorrelates the trees, so their errors don’t line up, and when their outputs are aggregated—by majority vote for classification or average for regression—the overall prediction tends to be more accurate and less prone to overfitting than a single decision tree.

This is different from boosting methods like gradient boosting or AdaBoost, which build trees sequentially to correct the mistakes of previous ones. It’s also a more specific form of bagging that uses decision trees as the base estimators and adds feature randomness, rather than applying bootstrap aggregation to arbitrary models.