Description: Bagging, or ‘Bootstrap Aggregating’, is an ensemble machine learning technique that improves the stability and accuracy of machine learning algorithms. Its main goal is to reduce the variance of models, which in turn helps to prevent overfitting. Bagging is based on the idea of creating multiple subsets of data from the original dataset through sampling with replacement. Each of these subsets is used to train an independent model. Subsequently, the predictions of all models are combined, typically by averaging in regression problems or by voting in classification problems. This technique is particularly useful in unstable algorithms, such as decision trees, where small variations in the data can lead to large changes in the model. By averaging the predictions of multiple models, bagging tends to produce a more robust and reliable model. Additionally, it is a technique that can be easily implemented in various machine learning frameworks, making it accessible to researchers and professionals in the field.
History: The bagging technique was introduced by Leo Breiman in 1994 as a way to improve the accuracy of machine learning models. Breiman proposed the use of bootstrap sampling to create multiple datasets from an original dataset, allowing for the training of several models and the combination of their predictions. This idea is part of the broader context of ensemble methods, which have evolved over the years and led to other techniques such as boosting and stacking.
Uses: Bagging is primarily used in classification and regression problems, where the goal is to improve the accuracy and stability of models. It is particularly effective in situations where individual models are prone to overfitting. It is applied in various fields, such as fraud detection, sales forecasting, and financial data analysis.
Examples: A classic example of bagging is the Random Forest algorithm, which uses multiple decision trees trained on different subsets of data. Another example is the use of bagging in image classification, where multiple models can be combined to improve accuracy in object identification.
