Grid Search CV is a popular hyperparameter tuning technique used in Machine Learning (ML) to optimize the performance of models. It is an exhaustive search method that evaluates a model’s performance on a given dataset by iterating over a predefined set of hyperparameters. The primary purpose of Grid Search CV is to identify the optimal combination of hyperparameters that result in the best model performance, typically measured by a chosen evaluation metric such as accuracy, precision, or F1-score.
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