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In a Spark MLlib project, you are working with a large dataset and need to build a decision tree model. Which of the following hyperparameters can be tuned to control the complexity of the decision tree and prevent overfitting?
A
The maximum depth of the tree, which determines the maximum number of levels in the tree.
B
The minimum number of instances required to split a node, which controls the minimum number of samples required to create a new branch.
C
The maximum number of bins used for discretizing continuous features, which determines the number of intervals used to categorize continuous variables.
D
All of the above, as tuning these hyperparameters can help control the complexity of the decision tree and prevent overfitting.
