How do I implement grid search for hyperparameter tuning?

How do I implement grid search for hyperparameter tuning?

To implement grid search, you can use libraries like Scikit-learn in Python. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

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

# Define the model
model = RandomForestClassifier()

# Define the parameter grid
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_depth': [None, 10, 20, 30]
}

# Set up the grid search
grid_search = GridSearchCV(model, param_grid, cv=5)

# Fit the model
grid_search.fit(X_train, y_train)

# Get the best parameters
print(grid_search.best_params_)

This will help you find the best hyperparameters for your model.

What does cv=5 mean in the code?

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