def METHOD_NAME(self):
    data = [
        {'sepal_length': 5, 'sepal_width': 3, 'petal_length': 1.5, 'petal_width': 0},
        {'sepal_length': 5, 'sepal_width': 2, 'petal_length': 5.0, 'petal_width': 1},
        {'sepal_length': 6, 'sepal_width': 3, 'petal_length': 4.8, 'petal_width': 2},
    ]
    # Test individual results
    self.check(
        '/model/iris/',
        method='put',
        data=json.dumps(
            {'url': 'iris.csv', 'model_class': 'sklearn.linear_model.SGDClassifier'}
        ),
        request_headers={'Model-Retrain': 'True'},
    )
    # In case model not trained, train it.
    single = [self.check('/model/iris/', method='post', data=json.dumps(k)) for k in data]
    single_responses = [response.json()[0] for response in single]
    ok_(
        all(
            response['result'] in {'setosa', 'versicolor', 'virginica'}
            for response in single_responses
        )
    )
    data_df = pd.DataFrame(data)
    # Test multiple results
    multi = self.check(
        '/model/iris/', method='post', data=json.dumps(data_df.to_dict(orient='list'))
    )
    eq_(multi.json(), single_responses)
    # This currently does not work since the model is not deterministic
    # Skip eq_([result['result'] for result in multi.json()],
    #          ['setosa', 'versicolor', 'virginica'])