Databricks Certified Machine Learning - Associate
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Describe the basics of Bayesian methods for tuning hyperparameters. Explain how Bayesian optimization works and its advantages over other hyperparameter tuning methods.
A
Bayesian optimization is a method for hyperparameter tuning that uses a probabilistic model to estimate the performance of a model with different hyperparameters. It iteratively selects hyperparameters to evaluate the model based on the probability of improving the current best performance. Bayesian optimization is more effective than other methods because it can efficiently explore the search space and find the optimal hyperparameters with fewer evaluations.
B
Bayesian optimization is a method for hyperparameter tuning that uses a deterministic model to estimate the performance of a model with different hyperparameters. It iteratively selects hyperparameters to evaluate the model based on the probability of improving the current best performance. Bayesian optimization is less effective than other methods because it can only explore the search space in a limited way and may not find the optimal hyperparameters.
C
Bayesian optimization is a method for hyperparameter tuning that uses a probabilistic model to estimate the performance of a model with different hyperparameters. It selects hyperparameters to evaluate the model based on a predefined grid. Bayesian optimization is less effective than other methods because it can only explore the search space in a limited way and may not find the optimal hyperparameters.
D
Bayesian optimization is a method for hyperparameter tuning that uses a deterministic model to estimate the performance of a model with different hyperparameters. It selects hyperparameters to evaluate the model based on a predefined grid. Bayesian optimization is more effective than other methods because it can efficiently explore the search space and find the optimal hyperparameters with fewer evaluations.