What are the best practices for tuning hyperparameters in machine learning?
An example of an AI chatbot conversation about best practices for tuning hyperparameters in ML.
What are the best practices for tuning hyperparameters in machine learning?
Some best practices include using grid search, random search, and Bayesian optimization to find optimal values.
Can you explain grid search and how it works?
Grid search involves defining a set of hyperparameters and their possible values, then evaluating all combinations.
What are the advantages of using random search over grid search?
Random search can be more efficient as it samples random combinations, often finding good results faster than grid search.
How do I know when to stop tuning hyperparameters?
You can stop tuning when the performance on a validation set no longer improves significantly or starts to overfit.
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