new_contents: import unittest
  
  import numpy as np
  from sklearn.model_selection import train_test_split
  
  from ngboost.distns import Bernoulli
  from ngboost.sklearn_api import NGBClassifier, NGBRegressor
  
  np.random.seed(1)
  
  
  class TestSklearn(unittest.TestCase):
      def test_classification(self):
          from sklearn.datasets import load_breast_cancer
          from sklearn.metrics import roc_auc_score
          data, target = load_breast_cancer(True)
          x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                              test_size=0.2,
                                                              random_state=42)
          ngb = NGBClassifier(Dist=Bernoulli, verbose=False)
          ngb.fit(x_train, y_train)
          preds = ngb.predict(x_test)
          score = roc_auc_score(y_test, preds)
          assert score >= 0.95
  
      def test_regression(self):
          from sklearn.datasets import load_boston
          from sklearn.metrics import mean_squared_error
          data, target = load_boston(True)
          x_train, x_test, y_train, y_test = train_test_split(data, target,
                                                              test_size=0.2,
                                                              random_state=42)
          ngb = NGBRegressor(verbose=False)
          ngb.fit(x_train, y_train)
          preds = ngb.predict(x_test)
          score = mean_squared_error(y_test, preds)
          assert score <= 8.0
  
subject: add test_with_sklearn Refernce: https://github.com/dmlc/xgboost/blob/4bbf062ed3bb27e0ce2daec0088fff699c8f8e15/tests/python/test_with_sklearn.py