Bagging
An ensemble learning technique that trains multiple independent models on random subsets of the training data and averages their final output predictions.
Think of It Like This
Like asking a diverse panel of doctors for their individual diagnoses and taking a majority vote to drastically reduce the chance of a misdiagnosis.
Short for Bootstrap Aggregating, this method drastically reduces the variance of high-complexity algorithms like decision trees. By introducing randomness into the data sampling process, each constituent model learns slightly different patterns. When combined, their collective prediction is significantly more robust against overfitting and outlier noise.