Grid search with Python

Grid search is a technique used to find the best hyperparameters for a machine learning model. It involves defining a grid of hyperparameters and searching for the optimal combination of these hyperparameters. First, we need to import the necessary libraries. We will use GridSearchCV from scikit-learn to perform grid search. 
from sklearn.model_selection import GridSearchCV

Next, we need to define the model and the hyperparameters grid. In this example, let's say we are using a Support Vector Machine (SVM) model and we want to tune the 'C' and 'kernel' hyperparameters. 
from sklearn.svm import SVC

model = SVC()
param_grid = {
    'C': [0.1, 1, 10],
    'kernel': ['linear', 'rbf']
}

Now, we can create a GridSearchCV object by passing the model, parameter grid, and the number of cross-validation folds. 
grid_search = GridSearchCV(model, param_grid, cv=3)

Next, we can fit the grid search object to our training data. 
grid_search.fit(X_train, y_train)

Finally, we can retrieve the best hyperparameters and the best score from the grid search results. 
best_params = grid_search.best_params_
best_score = grid_search.best_score_

print("Best Hyperparameters:", best_params)
print("Best Score:", best_score)

This is how we can perform grid search in Python to find the best hyperparameters for a machine learning model.

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