Random Forest
Random Forest is a machine learning algorithm that combines multiple decision trees to make predictions. It uses the outputs of many trees to produce a more robust prediction than a single decision tree.
What is Random Forest?
Random Forest is an ensemble learning method that trains multiple decision trees using different subsets of the training data and features. For classification, the trees vote on the predicted class, while for regression, their predictions are typically averaged to produce the final result.
Why is Random Forest Important?
Random Forest can reduce the risk of overfitting compared with individual decision trees and can handle complex relationships across many features. It also provides useful estimates of feature importance, making it practical for a wide range of structured-data problems.
Common use cases
Random Forest is commonly used for classification, regression, fraud detection, risk assessment, customer analysis, anomaly detection, and predictive modeling.