Boosting is the ensemble process of training machine learning models sequentially with each model being trained on a distinct subset of the data.

Boosting is the ensemble process of training a machine learning model for each sample in a set of bootstrapped samples of the training data, and then appending the model estimates as a feature variable on the training set which is used to train another model.