Bootstrap Aggregating

Bootstrap aggregating, commonly known as bagging, is a powerful ensemble machine learning technique designed to enhance the performance of models by reducing variance and avoiding overfitting. It involves creating multiple versions of a predictor model, each trained on a different random subset of the original dataset drawn with replacement. These subsets replicate the diversity and variability of the original dataset. By aggregating the predictions made by each model, bagging aims to produce a final prediction that is more stable and accurate than any single model could achieve. This method is particularly effective with high-variance, complex models like decision trees, where it significantly improves predictive performance by combining the strengths of multiple models to make more robust predictions.