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How would you describe the boosting technique in machine learning models?
A
Boosting is the ensemble process of training machine learning models sequentially with each model being trained on a distinct subset of the data.
B
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.
C
Boosting is the ensemble process of training machine learning models sequentially with each model learning from the errors of the preceding models.
D
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 combining the predictions of each model to get a final estimate.
E
Boosting is the ensemble process of training machine learning models sequentially with each model being trained on a progressively larger sample of the training data.
