Databricks-Machine-Learning-Professional Exam Dumps | A Data Scientist is training a binary classification model using LogisticRegression in SparkML on a large

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Question 66/96

A Data Scientist is training a binary classification model using LogisticRegression in SparkML on a large dataset stored in a Delta table. After fitting the pipeline, they want to evaluate the model's performance using an appropriate metric and scalable method across the distributed test data using the SparkML API. Which model evaluation strategy will suit their needs?

A. Use BinaryClassificationEvaluator with rawPredictionCo1 parameter, but first convert probability predictions to binary predictions using a UDF to ensure consistent evaluation across different Spark versions.

B. Convert the DataFrame to RDD and use MulticlassMetrics.confusionMatrix() to calculate precision and recall manually, as this avoids the overhead of DataFrame operations in distributed environments.

C. Use the BinaryClassificationEvaluator on the transformed test DataFrame and specify the appropriate metric.

D. Use MulticlassClassificationEvaluator with weightedPrecision metric since it provides more comprehensive evaluation than BinaryClassificationEvaluator for binary problems.

Question 66/96

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