👉 Bags engineering, also known as Bagging (Bootstrap Aggregating), is a machine learning technique used to improve the stability and accuracy of predictive models. It involves creating multiple subsets of the original training dataset through bootstrapping, which means sampling with replacement. Each subset is then used to train a separate model, typically of the same type (e.g., decision trees in the case of Random Forests). To make a final prediction, these individual models contribute their outputs, often through averaging for regression tasks or majority voting for classification tasks. This ensemble approach helps reduce variance and overfitting, leading to more robust and reliable predictions by leveraging the collective strength of multiple models.