Grid Search

 

Grid search is a powerful technique used for hyperparameter tuning in machine learning models. It allows us to systematically search through a grid of hyperparameter values and select the combination that yields the best performance on a validation set.

Here's how grid search works:

Define Hyperparameter Grid: First, we specify a grid of hyperparameter values that we want to explore. This grid is typically defined as a Python dictionary, where each key represents a hyperparameter name, and the corresponding value is a list of possible values for that hyperparameter.

Model Selection: Next, we select the machine learning model or estimator that we want to tune using grid search. This could be any model that has hyperparameters, such as ridge regression, support vector machines, random forests, etc.

Cross-Validation: We split our dataset into three parts: training set, validation set, and test set. The training set is used to train the models with different hyperparameter values, the validation set is used to evaluate the performance of each model, and the test set is used to assess the final model's performance.

Grid Search CV Object: We create a GridSearchCV object, passing in the model, the hyperparameter grid, and the number of cross-validation folds. This object will systematically evaluate the model performance for each combination of hyperparameters using cross-validation.

Fit Grid Search: We fit the GridSearchCV object to the training data. This will train multiple models with different hyperparameter values and evaluate their performance using cross-validation.

Select Best Parameters: After fitting, we can access the best hyperparameter values found by grid search using the best_params_ attribute of the GridSearchCV object.

Model Evaluation: Finally, we evaluate the performance of the model with the best hyperparameters on the test set to assess its generalization ability.

Overall, grid search automates the process of hyperparameter tuning, allowing us to efficiently explore different combinations of hyperparameters and select the optimal ones for our model.