What is automated hyperparameter tuning in machine learning?
Automated hyperparameter tuning is a process that optimizes the parameters of machine learning models automatically, improving performance.
Why is hyperparameter tuning important?
It's crucial because the right hyperparameters can significantly enhance model accuracy and efficiency, leading to better predictions.
What are some common methods for hyperparameter tuning?
Common methods include grid search, random search, and more advanced techniques like Bayesian optimization and genetic algorithms.
Can you explain grid search in more detail?
Sure! Grid search systematically explores a specified subset of hyperparameters by training the model on all combinations.
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