Boosting in mgo is a technique used to improve the performance of a machine learning model by training multiple models and combining their predictions. Each model is trained on a different subset of the data, and the predictions are then combined using a weighted average, with the weights determined by the performance of each model on a validation set.
Boosting can be used to improve the accuracy, robustness, and generalization performance of machine learning models. It is particularly effective for problems with high-dimensional data or a large number of features.
