from sklearn.model_selection import GridSearchCV

# Find best model settings
param_grid = {'n_estimators': [50, 100, 200]}
grid_search = GridSearchCV(RandomForestRegressor(), param_grid, cv=5)

grid_search.fit(X_train, y_train)
print(f"Best params: {grid_search.best_params_}")