Think Different

Bagging and boosting are both ensemble learning methods, which means they combine multiple models to create a more accurate and robust model than any single model could be. Bagging (short for bootstrap aggregating) works by creating multiple copies of the training dataset, each of which is created by sampling with a replacement from the original dataset. Each of these copies is then used to train a separate model, such as a decision tree or a linear regression model. The predictions of the individual models are then combined to create a final prediction. Bagging is effective at reducing the variance of a model, which is the tendency for a model to overfit the training data. This is because each of the individual models in the ensemble is trained on a different subset of the data, which helps to prevent them from all overfitting the same way. Boosting also works by creating multiple models, but it does so in a sequential manner. In the first iteration, a model is trained on the entir...