What is grid search in machine learning?
An example of an AI chatbot conversation about grid search for hyperparameter tuning.
What is grid search in machine learning?
Grid search is a technique used to find the best hyperparameters for a model by exhaustively searching through a specified subset of hyperparameters.
How does grid search work?
It works by defining a grid of hyperparameter values and evaluating the model's performance for each combination using cross-validation.
What are the advantages of using grid search?
The main advantages are its simplicity and the ability to systematically explore a wide range of hyperparameter values.
Are there any drawbacks to grid search?
Yes, it can be computationally expensive and may not be efficient for high-dimensional hyperparameter spaces.
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