What Random Forest Meaning, Applications & Example
Ensemble learning method using multiple decision trees.
What is Random Forest?
Random Forest is an ensemble learning method that combines multiple decision trees to improve predictive performance and control overfitting . Each tree is trained on a random subset of the data and makes independent predictions. The final output is the majority vote (for classification ) or average (for regression) of all the individual trees.
Key Features of Random Forest
- Bagging: It uses bootstrap aggregating (bagging) to create different subsets of the data, ensuring diversity among the trees.
- Random Feature Selection: For each tree, a random subset of features is considered when splitting nodes, which helps to reduce variance.
- Out-of-Bag Error Estimation: Random Forest estimates its own error by using the out-of-bag data, which is data not included in the training subset.
Applications of Random Forest
- Medical Diagnosis: Predicting diseases based on patient data by leveraging the ensemble nature to improve accuracy.
- Financial Market Prediction: Using historical data to forecast stock prices or market trends.
- Customer Segmentation: Identifying different customer segments in marketing to tailor strategies accordingly.
Example of Random Forest
In a credit scoring model , Random Forest can be used to predict whether a person will default on a loan. By aggregating predictions from multiple decision trees, it increases the model’s robustness and reduces the likelihood of overfitting, ensuring more accurate predictions.