What is Grid Search CV?
Grid Search CV is a hyperparameter tuning method that exhaustively searches through a specified hyperparameter grid to find the combination that yields the highest model performance. The "grid" in Grid Search CV refers to the predefined sets of hyperparameter values that we want to explore. For each combination of hyperparameters, Grid Search CV performs cross-validation to assess the model's performance on different subsets of the training data, thus providing a robust evaluation of the model's effectiveness.
How Grid Search CV Works:
- Define the Hyperparameter Grid: Before using Grid Search CV, we define a set of hyperparameters and their corresponding values that we want to explore. These hyperparameters act as the search space.
- Cross-Validation: Grid Search CV performs k-fold cross-validation on each combination of hyperparameters. It splits the training data into k subsets (folds) and trains the model k times, using different subsets as the validation data in each iteration.
- Model Evaluation: For each combination of hyperparameters, Grid Search CV calculates the average performance metric (e.g., accuracy, F1 score, etc.) across all k iterations. This metric serves as the evaluation score for that particular combination.
- Hyperparameters: Grid Search CV selects the combination of hyperparameters that yielded the highest evaluation score, representing the best-performing model within the specified hyperparameter grid.
Implementation
Grid Search CV plays a crucial role in fine-tuning our machine learning models, enabling us to achieve better performance and enhance the accuracy of our project's outcomes. By employing this technique, we ensure that our models are optimized and capable of delivering reliable results across different scenarios.