Databricks Machine Learning Associate Question 389
Select 2In a distributed environment like Apache Spark, how are decision trees scaled and applied in ensemble methods such as Random Forests?
- A
Each decision tree in the ensemble is trained independently on a subset of the data, leveraging distributed computation across Spark workers.
- B
Decision trees in Spark ensembles are trained sequentially on the driver node to ensure consistency in the model.
- C
Spark uses a data-parallel approach, splitting the data across partitions and training subtrees in parallel, which are later merged.
- D
The Random Forest algorithm in Spark uses distributed computations to handle multiple decision trees simultaneously across workers.
- E
Each tree in the ensemble is trained using the full dataset on a single executor to avoid communication overhead.
Show answer and explanation
Correct answers: A, D
Explanation
Spark scales decision trees in ensemble methods like Random Forests by distributing the training of individual trees across multiple workers. Each tree is trained independently on a bootstrap sample of the dataset, and Spark's distributed computations enable the simultaneous training of multiple trees, ensuring scalability and efficient use of resources. This approach is key to handling large-scale datasets in distributed environments.
- A. Correct.
Correct: Spark distributes the training of individual trees in the ensemble across its workers, allowing parallel computation on subsets of the data, which ensures scalability.
- B. Incorrect.
Incorrect: Decision trees in Spark ensembles are not trained sequentially or on the driver node, as this would not leverage Spark's distributed architecture.
- C. Incorrect.
Incorrect: Spark does not train subtrees in partitions and then merge them. Instead, each tree is trained independently on different data subsets.
- D. Correct.
Correct: Spark efficiently handles Random Forest training by distributing the computations for multiple trees across its workers, allowing simultaneous training.
- E. Incorrect.
Incorrect: Training each tree on the full dataset on a single executor would negate the benefits of Spark's distributed architecture and lead to inefficiency.