How do I use grid search for hyperparameters?

How do I use grid search for hyperparameters?

Grid search is a technique to find the best hyperparameters for your model by exhaustively searching through a specified subset of hyperparameters.

Can you give me an example of how to implement it in Python?

Sure! You can use the GridSearchCV class from sklearn.model_selection. Here's a basic example:

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

# Define the model
model = RandomForestClassifier()

# Define the hyperparameters to tune
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
}

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

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

# Get the best parameters
print(grid_search.best_params_)

What does the cv parameter do in GridSearchCV?

The cv parameter specifies the number of cross-validation folds to use. It helps in assessing the model's performance more reliably by training and validating it on different subsets of the data.