Explain the concept of grid-search in hyperparameter tuning, including how it works in conjunction with cross-validation. Provide a detailed explanation and include a hypothetical example of a parameter grid for a Random Forest model.

Simulated

Grid-search involves randomly selecting hyperparameter values and evaluating their performance.

0.0%

Grid-search with cross-validation systematically explores a predefined set of hyperparameter values to find the combination that maximizes model performance.

90.9%

Grid-search is used only for neural network models and not for other types of machine learning models.

0.0%

Grid-search does not require cross-validation and can be performed on a single train-test split.

9.1%

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Explain the concept of grid-search in hyperparameter tuning, including how it works in conjunction with cross-validation. Provide a detailed explanation and include a hypothetical example of a parameter grid for a Random Forest model.