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What is Understanding Random Forest in ML about?

Random Forest is a powerful supervised machine learning algorithm used for both regression and classification tasks, combining multiple decision trees to improve prediction accuracy. It employs ensemble techniques like Bagging and Boosting, with Bagging utilizing Bootstrap Aggregation to create independent models and Boosting focusing on correcting errors from previous models. The document also discusses the implementation of Random Forest using scikit-learn, highlighting its advantages over single decision

Author

24mt0362

Language

EN