Bagging, short for bootstrap aggregating, is an ensemble learning technique that aims to improve the stability and accuracy of machine learning models by combining the predictions of multiple base models. It is particularly effective when the base models are unstable or prone to overfitting.
The main idea behind bagging is to create several subsets of the training data through bootstrap sampling. Bootstrap sampling involves randomly sampling the training data with replacement, resulting in multiple subsets that are approximately the same size as the original dataset. Each subset is used to train a separate base model.
Here’s an overview of how the bagging algorithm works
Bootstrap sampling: Randomly select subsets (with replacement) from the original training data. Each subset will have the same size as the original dataset but may contain duplicate and missing samples.
Base model training: Train a base model on each of the bootstrap samples. Each base model is trained independently on a different subset of the training data.
Ensemble prediction: For a new input, obtain predictions from each base model. The final prediction is determined by aggregating the predictions of all base models. The aggregation can be done through voting (for classification tasks) or averaging (for regression tasks).
What are a few benefits of Bagging?
Reduced variance: By training multiple models on different subsets of the data, bagging helps to reduce the variance of the predictions. It reduces the impact of outliers or noisy samples, leading to more stable and robust predictions.
Improved generalization: Bagging can enhance the generalization performance of machine learning models by reducing overfitting. Since each base model is trained on a different subset of the data, they capture different aspects of the underlying patterns, resulting in a diverse ensemble that can generalize better to unseen data.
Model parallelization: Bagging allows the base models to be trained independently, making it possible to distribute the training process across multiple processors or machines. This parallelization can significantly speed up the training phase.
Popular algorithms that employ bagging include Random Forests, which use decision trees as base models, and Bagged Ensembles of any base model, such as Bagged SVM, Bagged Neural Networks, etc.
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