Random Forests: The Ensemble Powerhouse | Vibepedia

Random Forests are a powerful ensemble learning method that builds multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. Invented by Leo Breiman in 2001, this technique dramatically improves predictive accuracy and robustness by mitigating the overfitting tendencies of single decision trees. The 'randomness' comes from two key sources: bootstrap aggregating (bagging) of the training data for each tree, and random subspace method (random feature selection) at each split. This approach makes them a go-to for complex datasets, offering high performance with relatively little tuning, though their interpretability can be a challenge compared to simpler models.