Random Forest Regression

Random Forest Regression
Description	Random Forest Regression is an ensemble learning method that combines multiple decision trees to create a powerful predictive model for continuous target variables. It utilizes random feature selection to improve accuracy.
Why to use	When you want to predict a value depending on single or multiple independent variables.
When to use	High accuracy is required Reduced overfitting is required Large Datasets	When not to use	Training time is less Less memory
Prerequisites	If the data contains any missing values, use Missing Value Imputation before proceeding with Random Forest Regression.
Input	Any continuous large dataset	Output	Regression Key Performance Indicators (KPIs) Regression Statistics Actual Vs. Predicted scatter plot
Statistical Method Used	Bootstrap Sampling Decision Trees Ensemble Averaging Out of Bag Feature Importance Cross-Validation Hyperparameter Tuning	Limitations	Lack of interpretability Bias towards dominant classes Sensitivity to noisy data