diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index 078d106785e94..d010256e94345 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -54,33 +54,40 @@ the model and the data, like :func:`metrics.mean_squared_error`, are
 available as neg_mean_squared_error which return the negated value
 of the metric.
 
-
-============================    =========================================     ==================================
-Scoring                         Function                                      Comment
-============================    =========================================     ==================================
+==============================    =============================================     ==================================
+Scoring                           Function                                          Comment
+==============================    =============================================     ==================================
 **Classification**
-'accuracy'                      :func:`metrics.accuracy_score`
-'average_precision'             :func:`metrics.average_precision_score`
-'f1'                            :func:`metrics.f1_score`                      for binary targets
-'f1_micro'                      :func:`metrics.f1_score`                      micro-averaged
-'f1_macro'                      :func:`metrics.f1_score`                      macro-averaged
-'f1_weighted'                   :func:`metrics.f1_score`                      weighted average
-'f1_samples'                    :func:`metrics.f1_score`                      by multilabel sample
-'neg_log_loss'                  :func:`metrics.log_loss`                      requires ``predict_proba`` support
-'precision' etc.                :func:`metrics.precision_score`               suffixes apply as with 'f1'
-'recall' etc.                   :func:`metrics.recall_score`                  suffixes apply as with 'f1'
-'roc_auc'                       :func:`metrics.roc_auc_score`
+'accuracy'                        :func:`metrics.accuracy_score`
+'average_precision'               :func:`metrics.average_precision_score`
+'f1'                              :func:`metrics.f1_score`                          for binary targets
+'f1_micro'                        :func:`metrics.f1_score`                          micro-averaged
+'f1_macro'                        :func:`metrics.f1_score`                          macro-averaged
+'f1_weighted'                     :func:`metrics.f1_score`                          weighted average
+'f1_samples'                      :func:`metrics.f1_score`                          by multilabel sample
+'neg_log_loss'                    :func:`metrics.log_loss`                          requires ``predict_proba`` support
+'precision' etc.                  :func:`metrics.precision_score`                   suffixes apply as with 'f1'
+'recall' etc.                     :func:`metrics.recall_score`                      suffixes apply as with 'f1'
+'roc_auc'                         :func:`metrics.roc_auc_score`
 
 **Clustering**
-'adjusted_rand_score'           :func:`metrics.adjusted_rand_score`
+'adjusted_mutual_info_score'      :func:`metrics.adjusted_mutual_info_score`
+'adjusted_rand_score'             :func:`metrics.adjusted_rand_score`
+'completeness_score'              :func:`metrics.completeness_score`
+'fowlkes_mallows_score'           :func:`metrics.fowlkes_mallows_score`
+'homogeneity_score'               :func:`metrics.homogeneity_score`
+'mutual_info_score'               :func:`metrics.mutual_info_score`
+'normalized_mutual_info_score'    :func:`metrics.normalized_mutual_info_score`
+'v_measure_score'                 :func:`metrics.v_measure_score`
 
 **Regression**
-'neg_mean_absolute_error'       :func:`metrics.mean_absolute_error`
-'neg_mean_squared_error'        :func:`metrics.mean_squared_error`
-'neg_mean_squared_log_error'    :func:`metrics.mean_squared_log_error`
-'neg_median_absolute_error'     :func:`metrics.median_absolute_error`
-'r2'                            :func:`metrics.r2_score`
-============================    =========================================     ==================================
+'neg_mean_absolute_error'         :func:`metrics.mean_absolute_error`
+'neg_mean_squared_error'          :func:`metrics.mean_squared_error`
+'neg_mean_squared_log_error'      :func:`metrics.mean_squared_log_error`
+'neg_median_absolute_error'       :func:`metrics.median_absolute_error`
+'r2'                              :func:`metrics.r2_score`
+==============================    =============================================     ==================================
+
 
 Usage examples: