Bagging (Bootstrap Aggregating)

Information

• Bagging (Bootstrap Aggregating) is a machine learning technique that combines multiple models trained on different subsets of the training data.
• Bagging is often used to reduce the variance and improve the stability of the predictions.
• Bagging samples the training data with replacement to create multiple bootstrap samples, each of which has the same size as the original dataset.
• Bagging trains a separate model on each bootstrap sample, using the same model type and hyperparameters.
• The final prediction is a combination of the predictions of all the models, typically by averaging the predictions for regression problems or using voting for classification problems.
• Bagging can be used for both classification and regression problems.
• Random Forest is a popular bagging algorithm for decision trees, which randomly selects a subset of features at each split to decorrelate the trees and improve the generalization.
• Random Forest can handle high-dimensional data and non-linear relationships between the features and the target variable.
• Random Forest can also estimate the importance of each feature based on the reduction in impurity, and thus provide insights into the underlying data.
• Bagging algorithms have achieved state-of-the-art performance on many machine learning benchmarks and have been widely used in various applications, such as bioinformatics, finance, and e-commerce.