Random Forest Regression (RFR)

Random Forest Regression (RFR) is an Ensemble Learning Method that utilizes multiple Decision Trees to make predictions. Each tree in the Random Forest is built independently, and the final prediction is determined by averaging the predictions of all the individual trees. Through random sampling of the training data and features, along with the creation of a multitude of trees, RFR provides improved predictive accuracy and greater resistance to Overfitting compared to a single decision tree regression model.


  • RFR is capable of handling large datasets with high dimensionality and tends to be less sensitive to noisy data.
  • The random sampling of features at each node helps decorrelate the individual trees, enhancing the overall predictive power of the model.
  • The number of trees in the forest and the maximum depth of each tree are important hyperparameters that affect the model's performance.
  • Out-of-bag (OOB) error estimation and feature importance calculations are built-in features of the random forest algorithm.
  • Random forests are capable of performing well on both Regression and Classification tasks, making them versatile for a wide range of Predictive Analysis applications.