The provided code uses SparkTrials to parallelize the hyperparameter tuning process. SparkTrials is designed to distribute trials across a Spark cluster, where each trial runs as a separate Spark task. This is ideal for tuning single-node models (e.g., scikit-learn).

However, the question states the objective function wraps a Spark ML model. Spark ML models are inherently distributed and launch their own Spark jobs for training. Using SparkTrials with a Spark ML model would attempt to launch a Spark job from within another Spark task, a practice known as nested parallelism, which is not supported in Spark and can lead to deadlocks or failures.

The correct pattern for tuning Spark ML models is to run the tuning process on the driver node using the standard Trials() class. The driver will iterate through hyperparameter sets, and for each set, the Spark ML model training will be launched as a regular Spark job, utilizing the cluster as intended.

B. Reducing numevals only changes the number of tuning runs; it does not fix the fundamental architectural conflict of nested Spark jobs.

C. Changing fmin() to fmax() modifies the optimization objective (minimization vs. maximization) but does not address the execution model issue.

D. Removing the trials=trials argument would cause fmin to use a default Trials object, but the explicit and correct change is to replace SparkTrials with Trials.

E. Removing the algo=tpe.suggest argument changes the search algorithm but does not resolve the unsupported use of SparkTrials with a Spark ML model.

1. Databricks Official Documentation

"Hyperparameter tuning with Hyperopt": In the section on best practices

the documentation explicitly states the correct usage pattern.

Reference: Databricks Machine Learning Guide > Hyperparameter tuning > Best practices for Hyperopt.

Quote: "To tune Spark MLlib models

use Hyperopt on the driver node only; that is

do not use SparkTrials. For Spark MLlib models

Hyperopt generates hyperparameters on the driver and then Spark MLlib’s model fitting and evaluation algorithms use Spark to distribute the computation." This directly supports changing SparkTrials() to Trials() (which runs on the driver).

Quote: "When using SparkTrials

do not pass algorithms that require SparkContext to the objective function. For example

do not use CrossValidator or TrainValidationSplit to evaluate Spark ML models. These methods require running nested Spark jobs

which is not supported." This explains the underlying technical reason why the original code is incorrect.