Which approach does bagging utilize to enhance model accuracy?

Bagging, short for Bootstrap Aggregating, enhances model accuracy primarily by fitting multiple decision trees on different samples of the training data with replacement. This means that for each model (or tree) created, a new sample is drawn from the original dataset, allowing some data points to be repeated while others may not be used at all. As a result, each individual tree in the ensemble is likely to learn different aspects of the data and capture distinct patterns, which mitigates overfitting by averaging the predictions of these diverse models.

This approach reduces variance in predictions; by combining the outputs of multiple trees, bagging leads to a more stable and accurate overall model. When the separate models make predictions, the final prediction is typically determined by averaging (for regression tasks) or by majority voting (for classification tasks). This ensemble effect typically leads to better performance compared to individual models trained on the complete dataset.

While some other approaches exist for improving model accuracy, such as correcting errors from previous models or training entirely separate models on different datasets, bagging specifically relies on the methodology of sampling with replacement and leveraging the strength of multiple models trained on these varied samples.