code: def test_parameter_tuning():
      boston = load_boston()
      y = boston["target"]
      X = boston["data"]
      xgb_model = xgb.XGBRegressor()
      clf = GridSearchCV(
          xgb_model,
          {"max_depth": [2, 4, 6], "n_estimators": [50, 100, 200]},
          verbose=1,
      )
      clf.fit(X, y)
      assert clf.best_score_ < 0.7
      assert clf.best_params_ == {"n_estimators": 100, "max_depth": 4}
  
file_path: turicreate/src/external/xgboost/tests/python/test_with_sklearn.py
imports: import xgboost
  import numpy
  from sklearn.cross_validation import KFold, train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.grid_search import GridSearchCV
  from sklearn.datasets import load_iris, load_digits, load_boston
label_desc: Use image node_id 4 for calling a global function with example usage: test_parameter_tuning() without return types