Databricks Machine Learning Associate Question 388
Select 3You are working on a machine learning pipeline in Databricks to train a distributed decision tree model using Spark MLlib. The data consists of several terabytes and is stored in a distributed file system. How does Spark MLLib scale decision trees, and how does it handle ensembling methods like random forests or gradient-boosted trees?
- A
Spark uses data parallelism by splitting the data across the cluster and processing each partition independently.
- B
The decision tree algorithm in Spark scales by distributing nodes of the tree across different executors in the cluster.
- C
For ensembling methods like random forests, Spark trains multiple decision trees in parallel by distributing the training of each tree across the cluster.
- D
Gradient-boosted trees in Spark are trained sequentially, where each tree depends on the results of the previously trained tree.
- E
Spark does not support distributed training for decision tree-based algorithms like random forests or gradient-boosted trees.
Show answer and explanation
Correct answers: A, C, D
Explanation
Spark scales decision trees using data parallelism, where the data is split into partitions and processed independently across the cluster. For ensembling methods like random forests, Spark distributes the training of multiple trees across the cluster for parallelization. However, gradient-boosted trees must be trained sequentially, as each tree depends on the previous one. These techniques make Spark capable of handling large-scale decision tree-based models efficiently.
- A. Correct.
Correct: Spark uses data parallelism to split large datasets into smaller partitions, which are processed in parallel across cluster nodes. This is a key aspect of how Spark scales decision trees.
- B. Incorrect.
Incorrect: Spark does not distribute individual nodes of a decision tree across executors. The tree-building process is parallelized at the data level and, in the case of ensembles, at the tree level.
- C. Correct.
Correct: For random forests, Spark distributes the training of individual trees across the cluster, allowing multiple trees to be trained in parallel.
- D. Correct.
Correct: Gradient-boosted trees in Spark are trained sequentially because each tree is built to correct the errors of the previous one, which inherently prevents parallelization at the tree level.
- E. Incorrect.
Incorrect: Spark does support distributed training for decision tree-based algorithms, including random forests and gradient-boosted trees, making this statement false.