Use a single-threaded approach and sequentially run the hyperparameter tuning trials, as parallelization is not necessary for Spark ML models.

Parallelize the hyperparameter tuning process using Hyperopt's Trials, but limit the number of concurrent trials to match the available computational resources to avoid exceeding the budget.

Increase the number of trials exponentially to maximize the exploration of the hyperparameter space, regardless of the computational budget.

Focus solely on the initial choice of hyperparameters and avoid any hyperparameter tuning, as the computational budget is too limited for an effective optimization process.