Exam Databricks-Machine-Learning-Associate Topic 4 Question 3 Discussion

Actual exam question for Databricks's Databricks-Machine-Learning-Associate exam
Question #: 3
Topic #: 4

A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

A. Spark ML decision trees test every feature variable in the splitting algorithm B. Spark ML decision trees automatically prune overfit trees C. Spark ML decision trees test more split candidates in the splitting algorithm D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm E. Spark ML decision trees test binned features values as representative split candidates

Suggested Answer: E Vote an answer

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.
Reference:
Spark MLlib Documentation (Decision Trees and Quantile Binning).

by Nathaniel at Jul 21, 2025, 10:53 AM

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