DOC: trivial rst fix
@@ -8,12 +8,12 @@ used with a variety of estimators to do round-robin regression, treating every
variable as an output in turn.
In this example we compare some estimators for the purpose of missing feature
-imputation with :class:`sklearn.imputeIterativeImputer`::
+imputation with :class:`sklearn.imputeIterativeImputer`:
- :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression
- :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression
- :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R
- :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN
+* :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression
+* :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression
+* :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R
+* :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN
imputation approaches
Of particular interest is the ability of
