Random Forest Regression

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

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