About this document
Understanding Random Forest in ML by 24mt0362 is a document available to read on EtoBox.
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