Câu 29: Databricks Certified Machine Learning Associate

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult. Which of the following describes why?

1. A. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization.
2. B. Gradient boosting requires access to all data at once which cannot happen during parallelization.
3. C. Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.
4. D. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step. — đáp án hiện tại
5. E. Gradient boosting uses decision trees in each iteration which cannot be parallelized.