Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling.

Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models.

Bagging is performed in parallel.

In the context of machine learning, bagging, short for bootstrap aggregating, is a popular ensemble method used to improve the performance of machine learning models, particularly decision trees. Bagging involves training multiple instances of the same base learning algorithm (e.g., decision trees) on different subsets of the training data.

Here’s how bagging works:

Bootstrap Sampling: Given a dataset with N samples, bagging creates multiple random subsets (often of the same size as the original dataset) by sampling with replacement from the original dataset. This means that some samples may appear more than once in a subset, while others may not appear at all.
Model Training: A base learning algorithm (e.g., decision tree) is trained on each of these subsets independently. Since each subset is slightly different due to the random sampling, each model captures slightly different aspects of the data and may make different predictions.
Aggregation: Once all the models are trained, predictions from each individual model are combined to make the final prediction. The most common aggregation method for classification tasks is voting (where the mode of the predicted classes is taken), and for regression tasks, it’s averaging (where the mean of the predicted values is taken).

Bagging helps to reduce overfitting by reducing the variance of the model. By training multiple models on different subsets of data and aggregating their predictions, bagging produces a more stable and robust model that generalizes better to unseen data.

Key points to highlight when explaining bagging:

Bagging involves creating multiple subsets of the training data through bootstrap sampling.
Each subset is used to train a separate model (often of the same type).
Predictions from all models are combined (e.g., through voting or averaging) to make the final prediction.
Bagging helps to reduce overfitting and improve the stability and accuracy of the model.

In an interview context, it’s beneficial to provide examples and demonstrate understanding through clear explanations of how bagging improves model performance compared to using a single model. Additionally, discussing scenarios where bagging is particularly useful, such as with decision trees or unstable models, can further showcase your understanding.