How can I use Grid Search with Scikit-learn pipelines?

How can I use Grid Search with Scikit-learn pipelines?

You can use Grid Search to optimize hyperparameters in Scikit-learn pipelines by utilizing the GridSearchCV class.

Can you show me a code example?

Sure! Here's a simple example:

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV

pipeline = Pipeline([
    ('classifier', RandomForestClassifier())
])

param_grid = {
    'classifier__n_estimators': [50, 100],
    'classifier__max_depth': [None, 10, 20]
}

grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(X_train, y_train)

What does cv=5 mean in this context?

The cv=5 parameter specifies that 5-fold cross-validation will be used to evaluate the performance of the model during the grid search.

How do I access the best parameters after the search?

You can access the best parameters using grid_search.best_params_ after fitting the model.