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You plan to use hyperparameter tuning to find optimal discrete values for a set of hyperparameters. You want to try every possible combination of a set of specified discrete values. Which kind of sampling should you use?
Random sampling
Grid sampling
Bayesian sampling
You're using hyperparameter tuning to train an optimal model based on a target metric named "AUC". What should you do in your training script?
Import the logging package and use a logging.info() statement to log the AUC.
Include a print() statement to write the AUC value to the standard output stream.
Use a mlflow.log_metric() statement to log the AUC value.
You must answer all questions before checking your work.
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