Gradient boosting means combining weak and average predictors to acquire one strong predictor. This ensures robustness. It is similar to a random forest, which is mainly based on decision trees. The difference is that the sample is not modified from one tree to another; only the weights of the different observations are modified.
Boosting trains trees sequentially by using information from previously trained trees. For this, we first need to create decision trees using the training dataset. Then, we need to create another model that does nothing but rectify the errors that occurred in the training model. This process is repeated sequentially until the specified number of trees, or some other stopping rule, is reached.
More specific details about the algorithm can be found in the documentation of the h2o package. While training the algorithm, we will need to define...