Merge d9a32c4 into e155d86

doc/quickstart.rst

@@ -31,8 +31,8 @@ The next element is the data stream that we aim to process. In the example we wi
 The third requirement of the experiment is to specify the metrics used in the evaluation of the methods. In the example, we will use the *accuracy* metric available in ``scikit-learn`` and the *balanced accuracy* from the ``stream-learn`` module::
 
   from sklearn.metrics import accuracy_score
-  from strlearn.utils.metrics import bac
-  metrics = [accuracy_score, bac]
+  from strlearn.metrics import balanced_accuracy_score
+  metrics = [accuracy_score, balanced_accuracy_score]
 
 The last necessary element of processing is the evaluator, i.e. the method of conducting the experiment. For example, we will choose the *Test-Then-Train* paradigm, described in more detail in `User Guide <evaluators.html>`_. It is important to note, that we need to provide the metrics that we will use in processing at the point of initializing the evaluator. In the case of none metrics given, it will use default pair of *accuracy* and *balanced accuracy* scores::