scikit-learn
diff --git a/‎doc/whats_new/v1.6.rst
+8-2 b/‎doc/whats_new/v1.6.rst
+8-2
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
+6-4 b/‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
+6-4
diff --git a/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+1-1 b/‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
+1-1
@@ -71,7 +71,7 @@ more details.
   :class:`ensemble.StackingRegressor` now support metadata routing and pass
   ``**fit_params`` to the underlying estimators via their `fit` methods.
   :pr:`28701` by :user:`Stefanie Senger <StefanieSenger>`.
-  
+
 - |Feature| :class:`compose.TransformedTargetRegressor` now supports metadata
   routing in its `fit` and `predict` methods and routes the corresponding
   params to the underlying regressor.
@@ -142,6 +142,12 @@ Changelog
   by parallelizing the initial search for bin thresholds
   :pr:`28064` by :user:`Christian Lorentzen <lorentzenchr>`.
 
+- |Enhancement| The verbosity of :class:`ensemble.HistGradientBoostingClassifier`
+  and :class:`ensemble.HistGradientBoostingRegressor` got a more granular control. Now,
+  `verbose = 1` prints only summary messages, `verbose >= 2` prints the full
+  information as before.
+  :pr:`28179` by :user:`Christian Lorentzen <lorentzenchr>`.
+
 - |Efficiency| :class:`ensemble.IsolationForest` now runs parallel jobs
   during :term:`predict` offering a speedup of up to 2-4x on sample sizes
   larger than 2000 using `joblib`.
@@ -150,7 +156,7 @@ Changelog
 
 - |Feature| :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor` now support
   missing-values in the data matrix `X`. Missing-values are handled by randomly moving all of
-  the samples to the left, or right child node as the tree is traversed. 
+  the samples to the left, or right child node as the tree is traversed.
   :pr:`28268` by :user:`Adam Li <adam2392>`.
 
 :mod:`sklearn.impute`
 
@@ -857,7 +857,7 @@ def fit(self, X, y, sample_weight=None):
         )
 
         for iteration in range(begin_at_stage, self.max_iter):
-            if self.verbose:
+            if self.verbose >= 2:
                 iteration_start_time = time()
                 print(
                     "[{}/{}] ".format(iteration + 1, self.max_iter), end="", flush=True
@@ -987,7 +987,7 @@ def fit(self, X, y, sample_weight=None):
                         raw_predictions_val=raw_predictions_val,
                     )
 
-            if self.verbose:
+            if self.verbose >= 2:
                 self._print_iteration_stats(iteration_start_time)
 
             # maybe we could also early stop if all the trees are stumps?
@@ -1617,7 +1617,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         iterations to be considered an improvement upon the reference score.
     verbose : int, default=0
         The verbosity level. If not zero, print some information about the
-        fitting process.
+        fitting process. ``1`` prints only summary info, ``2`` prints info per
+        iteration.
     random_state : int, RandomState instance or None, default=None
         Pseudo-random number generator to control the subsampling in the
         binning process, and the train/validation data split if early stopping
@@ -1996,7 +1997,8 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting):
         considered an improvement upon the reference score.
     verbose : int, default=0
         The verbosity level. If not zero, print some information about the
-        fitting process.
+        fitting process. ``1`` prints only summary info, ``2`` prints info per
+        iteration.
     random_state : int, RandomState instance or None, default=None
         Pseudo-random number generator to control the subsampling in the
         binning process, and the train/validation data split if early stopping
 
@@ -160,7 +160,7 @@ def test_early_stopping_classification(
     X, y = data
 
     gb = HistGradientBoostingClassifier(
-        verbose=1,  # just for coverage
+        verbose=2,  # just for coverage
         min_samples_leaf=5,  # easier to overfit fast
         scoring=scoring,
         tol=tol,