Abbreviation for Bootstrap Aggregating, it is an ensemble technique in machine learning that integrates multiple models to increase stability and accuracy. First, multiple training data subsets are generated from the original dataset through sampling with replacement. This means that some examples may appear in several subsets, while others may not appear at all. Subsequently, a learning model is trained on each of these data subsets. These models can be of the same type, such as decision trees, but are trained with different data subsets.
Once all models are trained, aggregated prediction is performed. For classification problems, predictions are obtained through majority voting among the models. For regression problems, predictions are averaged.
