Why the bagging ensemble leads to a more sensible decision boundary?
时间: 2024-05-21 17:14:52 浏览: 48
The bagging ensemble method can lead to a more sensible decision boundary because it reduces overfitting and variance in the model. Bagging involves creating multiple models using bootstrap samples of the training data, and then combining the predictions of these models. By using multiple models, bagging can reduce the impact of outliers and noise in the training data.
When these models are combined, the decision boundary is more likely to be a smooth, well-behaved function that generalizes well to new data. This is because each model only has access to a subset of the training data, and therefore is less likely to fit noise or outliers.
Overall, the bagging ensemble method can lead to a more sensible decision boundary because it reduces overfitting and variance in the model, resulting in a smoother and more robust decision boundary.