diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index 5c94569734678..d771b8e3da9de 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -2481,9 +2481,15 @@ class labels (multi-output problem).
             dict,
             list,
             None,
-        ],
+        ]
     }
     _parameter_constraints.pop("splitter")
+    _parameter_constraints.pop("max_samples")
+    _parameter_constraints["max_samples"] = [
+        None,
+        Interval(RealNotInt, 0.0, None, closed="right"),
+        Interval(Integral, 1, None, closed="left"),
+    ]
 
     def __init__(
         self,
@@ -2509,7 +2515,9 @@ def __init__(
         max_samples=None,
         max_bins=None,
         store_leaf_values=False,
-        monotonic_cst=None
+        monotonic_cst=None,
+        stratify=False,
+        honest_prior="ignore"
     ):
         self.target_tree_kwargs = {
             "criterion": criterion,
@@ -2528,7 +2536,9 @@ def __init__(
         super().__init__(
             estimator=HonestDecisionTree(
                 target_tree_class=target_tree_class,
-                target_tree_kwargs=self.target_tree_kwargs
+                target_tree_kwargs=self.target_tree_kwargs,
+                stratify=stratify,
+                honest_prior=honest_prior
             ),
             n_estimators=n_estimators,
             estimator_params=(
@@ -2572,6 +2582,8 @@ def __init__(
         self.monotonic_cst = monotonic_cst
         self.ccp_alpha = ccp_alpha
         self.target_tree_class = target_tree_class
+        self.stratify = stratify
+        self.honest_prior = honest_prior
 
 
 class RandomForestRegressor(ForestRegressor):
diff --git a/sklearn/tree/_honest_tree.py b/sklearn/tree/_honest_tree.py
index b0b5ddcde3839..a7a3d59d7b00b 100644
--- a/sklearn/tree/_honest_tree.py
+++ b/sklearn/tree/_honest_tree.py
@@ -24,9 +24,9 @@
 class HonestDecisionTree(BaseDecisionTree):
     _parameter_constraints: dict = {
         **BaseDecisionTree._parameter_constraints,
-        "target_tree_class": [BaseDecisionTree],
+        "target_tree_class": "no_validation",
         "target_tree_kwargs": [dict],
-        "honest_fraction": [Interval(RealNotInt, 0.0, 1.0, closed="neither")],
+        "honest_fraction": [Interval(RealNotInt, 0.0, 1.0, closed="both")],
         "honest_prior": [StrOptions({"empirical", "uniform", "ignore"})],
         "stratify": ["boolean"],
     }
@@ -221,7 +221,7 @@ def fit(
 
         # fingers crossed sklearn.utils.validation.check_is_fitted doesn't
         # change its behavior
-        print(f"n_classes = {target_bta.n_classes}")
+        #print(f"n_classes = {target_bta.n_classes}")
         self.tree_ = HonestTree(
             self.target_tree.n_features_in_,
             target_bta.n_classes,
@@ -231,8 +231,8 @@ def fit(
         self.honesty.resize_tree(self.tree_, self.honesty.get_node_count())
         self.tree_.node_count = self.honesty.get_node_count()
 
-        print(f"dishonest node count = {self.target_tree.tree_.node_count}")
-        print(f"honest node count = {self.tree_.node_count}")
+        #print(f"dishonest node count = {self.target_tree.tree_.node_count}")
+        #print(f"honest node count = {self.tree_.node_count}")
 
         criterion = BaseDecisionTree._create_criterion(
             self.target_tree,
@@ -250,8 +250,8 @@ def fit(
 
         for i in range(self.honesty.get_node_count()):
             start, end = self.honesty.get_node_range(i)
-            print(f"setting sample range for node {i}: ({start}, {end})")
-            print(f"node {i} is leaf: {self.honesty.is_leaf(i)}")
+            #print(f"setting sample range for node {i}: ({start}, {end})")
+            #print(f"node {i} is leaf: {self.honesty.is_leaf(i)}")
             self.honesty.set_sample_pointers(criterion, start, end)
 
             if missing_values_in_feature_mask is not None: