DOC n_classes -> #classes (#11702)

jnothman · web-flow · commit 89b25a586e77 · 2018-07-29T23:05:44.000+10:00
diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
@@ -440,10 +440,10 @@ the total number of predictions).
 
 In contrast, if the conventional accuracy is above chance only because the
 classifier takes advantage of an imbalanced test set, then the balanced
-accuracy, as appropriate, will drop to :math:`\frac{1}{\text{n\_classes}}`.
+accuracy, as appropriate, will drop to :math:`\frac{1}{\text{#classes}}`.
 
 The score ranges from 0 to 1, or when ``adjusted=True`` is used, it rescaled to
-the range :math:`\frac{1}{1 - \text{n\_classes}}` to 1, inclusive, with
+the range :math:`\frac{1}{1 - \text{#classes}}` to 1, inclusive, with
 performance at random scoring 0.
 
 If :math:`y_i` is the true value of the :math:`i`-th sample, and :math:`w_i`
@@ -463,7 +463,7 @@ defined as:
 
 With ``adjusted=True``, balanced accuracy reports the relative increase from
 :math:`\texttt{balanced-accuracy}(y, \mathbf{0}, w) =
-\frac{1}{\text{n\_classes}}`.  In the binary case, this is also known as
+\frac{1}{\text{#classes}}`.  In the binary case, this is also known as
 `*Youden's J statistic* <https://en.wikipedia.org/wiki/Youden%27s_J_statistic>`_,
 or *informedness*.
 
