What is grid search in machine learning?
Grid search is a technique used to find the best hyperparameters for a machine learning model by exhaustively searching through a specified subset of hyperparameters.
How does grid search work?
Grid search works by defining a grid of hyperparameter values and then training the model on each combination of these values to evaluate performance.
What are the advantages of using grid search?
The main advantages are that it provides a systematic way to explore hyperparameter combinations and can lead to improved model performance.
Are there any alternatives to grid search?
Yes, alternatives include random search and Bayesian optimization, which can be more efficient in certain scenarios.
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