Databricks Certified Machine Learning - Associate
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Consider a scenario where you are using AutoML to build a classification model, and you want to optimize the model's performance. Explain the role of hyperparameter tuning in AutoML and how it can be used to optimize the model's hyperparameters. Provide a detailed explanation of the techniques used by AutoML for hyperparameter tuning and their significance in improving model performance.
A
Hyperparameter tuning is not necessary in AutoML, as the algorithm automatically selects the best hyperparameters for the model.
B
Hyperparameter tuning is important in AutoML, but it is performed manually by the user by trying different combinations of hyperparameters and selecting the best combination.
C
Hyperparameter tuning is important in AutoML, and it can be automated using techniques such as grid search, random search, and Bayesian optimization to find the optimal hyperparameters for the model.
D
Hyperparameter tuning is not relevant for classification models in AutoML, as the algorithm automatically selects the best model architecture and hyperparameters.