Databricks Certified Machine Learning - Associate
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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?
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?
Simulated
A
The maximum depth of the tree, which determines the maximum number of levels in the tree.
8.2%
B
The minimum number of instances required to split a node, which controls the minimum number of samples required to create a new branch.
5.4%
C
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