How bagging reduces variance error and Boosting bias error?
Bagging and Boosting decrease the variance of a single estimate as they combine several estimates from different models. As a result, the performance of the model increases, and the predictions are much more robust and stable. If a single model gets a low performance, Bagging rarely gets a better bias.
How does bagging reduce the variance?
Bootstrap aggregation, or “bagging,” in machine learning decreases variance through building more advanced models of complex data sets. Since this approach consolidates discovery into more defined boundaries, it decreases variance and helps with overfitting.
How to reduce the variance while retaining the bias?
A key insight for ensembling predictors is that by averaging (or generally aggregating) many low bias, high variance predictors, we can reduce the variance while retaining the low bias. Here’s an example of this for density estimation:
How to reduce the variance of a prediction?
If we want to reduce the amount of variance in a prediction, we must add bias. Consider the case of a simple statistical estimate of a population parameter, such as estimating the mean from a small random sample of data. A single estimate of the mean will have high variance and low bias.
What’s the effect of bagging with linear regression?
The good thing about Bagging is, that it also does not increase the bias again, which we will motivate in the following section. That is why the effect of using Bagging together with Linear Regression is low: You can not decrease the bias via Bagging, but with Boosting.
When does bagging do not improve the outcome?
If all the models trained initially have a high bias, bagging cannot improve the scenario. If all models learnt initially don’t fit the training data well, they will not fit the training (or test) data when combined. Finally, hardware built and configured by ML experts.