Helpful examples of using Bagging machine learning algorithms in scikit-learn.
The Bagging (Bootstrap Aggregating) algorithm is an ensemble learning method designed to improve the stability and accuracy of machine learning models, particularly those prone to overfitting, such as decision trees.
The core idea is to create multiple versions of a predictor and use these to get an aggregated prediction. It works by generating multiple subsets of the training data through bootstrap sampling, where each subset is created by randomly sampling with replacement. A separate model is then trained on each subset.
For classification tasks, the final prediction is made by majority voting among the models, while for regression, the predictions are averaged.
Bagging helps to reduce variance and improve the model’s robustness and accuracy by smoothing out anomalies and noise in the training data.
The Random Forest algorithm is a popular extension of bagging that involves training multiple decision trees with added randomness, enhancing the benefits of the bagging technique.