Bootstrap Aggregating (Bagging)

Bootstrap Aggregating, or Bagging, is a technique that improves the accuracy of machine learning models by combining multiple versions of a model trained on different subsets of data. It works by repeatedly sampling data with replacement (so some data points may be used multiple times) to create diverse training sets. Each model is trained separately, and their predictions are then averaged or voted on to produce a final result. This process reduces overfitting and increases stability, helping the overall model make better, more reliable predictions, especially when data or the model itself is noisy.