code: def test_binary_classification():
      digits = load_digits(2)
      y = digits["target"]
      X = digits["data"]
      kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng)
      for train_index, test_index in kf:
          xgb_model = xgb.XGBClassifier().fit(
              X[train_index], y[train_index]
          )
          preds = xgb_model.predict(X[test_index])
          labels = y[test_index]
          err = sum(
              1
              for i in range(len(preds))
              if int(preds[i] > 0.5) != labels[i]
          ) / float(len(preds))
      assert err < 0.1
  
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 1 for calling a global function with example usage: test_binary_classification() without return types