Mastering Predictive Analytics with scikit-learn and TensorFlow

Bagging

Bagging, also known as bootstrap aggregation, is a general purpose procedure for reducing variance in the machine learning model. It is based on the bootstrap sampling technique and is generally used with regression or classification trees, but in principle this bagging technique can be used with any model.

The following steps are involved in the bagging process:

1. We choose the number of estimators or individual models to use. Let's consider this as parameter B.
2. We take sample datasets from B with replacement using the bootstrap sampling from the training set.
3. For every one of these training datasets, we fit the machine learning model in each of the bootstrap samples. This way, we get individual predictors for the B parameter.
4. We ...