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=== Random forest regression === Random forest regression (RFR) falls under umbrella of decision [[tree-based models]]. RFR is an ensemble learning method that builds multiple decision trees and averages their predictions to improve accuracy and to avoid overfitting. Β To build decision trees, RFR uses bootstrapped sampling, for instance each decision tree is trained on random data of from training set. This random selection of RFR for training enables model to reduce bias predictions and achieve accuracy. RFR generates independent decision trees, and it can work on single output data as well multiple regressor task. This makes RFR compatible to be used in various application.<ref>{{Cite web |title=RandomForestRegressor |url=https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html |access-date=12 February 2025 |website=scikit-learn |language=en}}</ref><ref>{{Cite web |date=20 October 2021 |title=What Is Random Forest? {{!}} IBM |url=https://www.ibm.com/think/topics/random-forest |access-date=12 February 2025 |website=www.ibm.com |language=en}}</ref>
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