deepatdotnet
diff --git a/‎doc/modules/model_evaluation.rst
Lines changed: 10 additions & 7 deletions b/‎doc/modules/model_evaluation.rst
Lines changed: 10 additions & 7 deletions
@@ -297,7 +297,7 @@ In this context, we can define the notions of precision, recall and F-measure:
 
    F_\beta = (1 + \beta^2) \frac{\text{precision} \times \text{recall}}{\beta^2 \text{precision} + \text{recall}}.
 
-Here some small examples in binary classification:
+Here some small examples in binary classification::
 
   >>> from sklearn import metrics
   >>> y_pred = [0, 1, 0, 0]
@@ -411,7 +411,7 @@ their support
 
   \texttt{weighted\_{}F\_{}beta}(y,\hat{y}) &= \frac{1}{n_\text{samples}} \sum_{i=0}^{n_\text{samples} - 1} (1 + \beta^2)\frac{|y_i \cap \hat{y}_i|}{\beta^2 |\hat{y}_i| + |y_i|}.
 
-Here an example where ``average`` is set to ``average`` to ``macro``:
+Here an example where ``average`` is set to ``average`` to ``macro``::
 
   >>> from sklearn import metrics
   >>> y_true = [0, 1, 2, 0, 1, 2]
@@ -427,7 +427,7 @@ Here an example where ``average`` is set to ``average`` to ``macro``:
   >>> metrics.precision_recall_fscore_support(y_true, y_pred, average='macro')  # doctest: +ELLIPSIS
   (0.22..., 0.33..., 0.26..., None)
 
-Here an example where ``average`` is set to to ``micro``:
+Here an example where ``average`` is set to to ``micro``::
 
   >>> from sklearn import metrics
   >>> y_true = [0, 1, 2, 0, 1, 2]
@@ -443,7 +443,7 @@ Here an example where ``average`` is set to to ``micro``:
   >>> metrics.precision_recall_fscore_support(y_true, y_pred, average='micro')  # doctest: +ELLIPSIS
   (0.33..., 0.33..., 0.33..., None)
 
-Here an example where ``average`` is set to to ``weighted``:
+Here an example where ``average`` is set to to ``weighted``::
 
   >>> from sklearn import metrics
   >>> y_true = [0, 1, 2, 0, 1, 2]
@@ -459,7 +459,7 @@ Here an example where ``average`` is set to to ``weighted``:
   >>> metrics.precision_recall_fscore_support(y_true, y_pred, average='weighted')  # doctest: +ELLIPSIS
   (0.22..., 0.33..., 0.26..., None)
 
-Here an example where ``average`` is set to ``None``:
+Here an example where ``average`` is set to ``None``::
 
   >>> from sklearn import metrics
   >>> y_true = [0, 1, 2, 0, 1, 2]
@@ -492,7 +492,7 @@ value and :math:`w` is the predicted decisions as output by
   L_\text{Hinge}(y, w) = \max\left\{1 - wy, 0\right\} = \left|1 - wy\right|_+
 
 Here a small example demonstrating the use of the :func:`hinge_loss` function
-with a svm classifier:
+with a svm classifier::
 
   >>> from sklearn import svm
   >>> from sklearn.metrics import hinge_loss
@@ -613,6 +613,9 @@ where :math:`1(x)` is the `indicator function
   >>> y_pred = [1, 2, 3, 4]
   >>> y_true = [2, 2, 3, 4]
+
+G
+
   >>> zero_one_loss(y_true, y_pred)
   0.25
   >>> zero_one_loss(y_true, y_pred, normalize=False)
@@ -653,7 +656,7 @@ variance is  estimated  as follow:
 
 The best possible score is 1.0, lower values are worse.
 
-Here a small example of usage of the :func:`explained_variance_scoreé`
+Here a small example of usage of the :func:`explained_variance_score`
 function::
 
     >>> from sklearn.metrics import explained_variance_score