What limitation does the "timeout" parameter impose on the trials in SparkTrials?

The "timeout" parameter in SparkTrials serves to prevent excessive resource consumption during the hyperparameter tuning process. By setting a time limit on how long each trial can run, it ensures that resources are allocated efficiently and that potentially long-running trials do not waste computational power and time. This is especially important in distributed environments like Apache Spark, where multiple trials may be running concurrently. By limiting the duration of these trials, users can avoid situations where some models might take significantly longer to evaluate than others, enabling a more balanced and efficient use of resources throughout the tuning process.

The other options do not accurately reflect the purpose of the "timeout" parameter. The restriction on input features, limitations on the types of models, and the amount of metadata logged are unrelated to the intention behind imposing a timeout on trials in SparkTrials.