diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst
index 1d1f6e926e8f8..45523d74fe9b8 100644
--- a/doc/modules/impute.rst
+++ b/doc/modules/impute.rst
@@ -106,16 +106,17 @@ round are returned.
     >>> import numpy as np
     >>> from sklearn.impute import IterativeImputer
     >>> imp = IterativeImputer(n_iter=10, random_state=0)
-    >>> imp.fit([[1, 2], [np.nan, 3], [7, np.nan]])  # doctest: +NORMALIZE_WHITESPACE
+    >>> imp.fit([[1, 2], [3, 6], [4, 8], [np.nan, 3], [7, np.nan]])  # doctest: +NORMALIZE_WHITESPACE
     IterativeImputer(imputation_order='ascending', initial_strategy='mean',
         max_value=None, min_value=None, missing_values=nan, n_iter=10,
         n_nearest_features=None, predictor=None, random_state=0,
         sample_posterior=False, verbose=False)
     >>> X_test = [[np.nan, 2], [6, np.nan], [np.nan, 6]]
+    >>> # the model learns that the second feature is double the first
     >>> print(np.round(imp.transform(X_test)))
     [[ 1.  2.]
-     [ 6.  3.]
-     [26.  6.]]
+     [ 6. 12.]
+     [ 3.  6.]]
 
 Both :class:`SimpleImputer` and :class:`IterativeImputer` can be used in a Pipeline
 as a way to build a composite estimator that supports imputation.