diff --git a/doc/sphinxext/allow_nan_estimators.py b/doc/sphinxext/allow_nan_estimators.py
index 45cbf9627..9fc4e2d0f 100755
--- a/doc/sphinxext/allow_nan_estimators.py
+++ b/doc/sphinxext/allow_nan_estimators.py
@@ -3,8 +3,8 @@
 from docutils import nodes
 from docutils.parsers.rst import Directive
 from sklearn.utils import all_estimators
+from sklearn.utils._test_common.instance_generator import _construct_instances
 from sklearn.utils._testing import SkipTest
-from sklearn.utils.estimator_checks import _construct_instance
 
 
 class AllowNanEstimators(Directive):
diff --git a/treeple/_lib/sklearn_fork b/treeple/_lib/sklearn_fork
index e4b9728cb..4fd15fdf8 160000
--- a/treeple/_lib/sklearn_fork
+++ b/treeple/_lib/sklearn_fork
@@ -1 +1 @@
-Subproject commit e4b9728cb8667d0a40ed0c6c45f0414811f5f1f8
+Subproject commit 4fd15fdf88737e7e84e96217b2c9b0ce0c162c2c
diff --git a/treeple/ensemble/_honest_forest.py b/treeple/ensemble/_honest_forest.py
index 447371b37..0650d9a31 100644
--- a/treeple/ensemble/_honest_forest.py
+++ b/treeple/ensemble/_honest_forest.py
@@ -10,7 +10,7 @@
 from sklearn.base import _fit_context, clone
 from sklearn.ensemble._base import _partition_estimators, _set_random_states
 from sklearn.utils import compute_sample_weight, resample
-from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions
+from sklearn.utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
 from sklearn.utils.validation import check_is_fitted
 
 from .._lib.sklearn.ensemble._forest import ForestClassifier
@@ -417,11 +417,18 @@ class labels (multi-output problem).
         Interval(RealNotInt, 0.0, None, closed="right"),
         Interval(Integral, 1, None, closed="left"),
     ]
-    _parameter_constraints["honest_fraction"] = [Interval(RealNotInt, 0.0, 1.0, closed="both")]
-    _parameter_constraints["honest_prior"] = [
-        StrOptions({"empirical", "uniform", "ignore"}),
-    ]
-    _parameter_constraints["stratify"] = ["boolean"]
+    _parameter_constraints.update(
+        {
+            "tree_estimator": [
+                HasMethods(["fit", "predict", "predict_proba", "apply"]),
+                None,
+            ],
+            "honest_fraction": [Interval(RealNotInt, 0.0, 1.0, closed="neither")],
+            "honest_prior": [StrOptions({"empirical", "uniform", "ignore"})],
+            "stratify": ["boolean"],
+            "tree_estimator_params": ["dict"],
+        }
+    )
 
     def __init__(
         self,
diff --git a/treeple/stats/forest.py b/treeple/stats/forest.py
index 5b5b0b056..34ea690b5 100644
--- a/treeple/stats/forest.py
+++ b/treeple/stats/forest.py
@@ -1,6 +1,7 @@
 import threading
 from collections import namedtuple
 from typing import Callable
+from warnings import warn
 
 import numpy as np
 from joblib import Parallel, delayed
@@ -10,6 +11,8 @@
 from sklearn.utils.multiclass import type_of_target
 
 from .._lib.sklearn.ensemble._forest import ForestClassifier
+from ..ensemble import HonestForestClassifier
+from ..tree import MultiViewDecisionTreeClassifier
 from ..tree._classes import DTYPE
 from .permuteforest import PermutationHonestForestClassifier
 from .utils import METRIC_FUNCTIONS, POSITIVE_METRICS, _compute_null_distribution_coleman
@@ -38,8 +41,8 @@ def _parallel_predict_proba_oob(predict_proba, X, out, idx, test_idx, lock):
 
 
 def build_coleman_forest(
-    est,
-    perm_est,
+    est: HonestForestClassifier,
+    perm_est: PermutationHonestForestClassifier,
     X,
     y,
     covariate_index=None,
@@ -111,6 +114,9 @@ def build_coleman_forest(
     """
     metric_func: Callable[[ArrayLike, ArrayLike], float] = METRIC_FUNCTIONS[metric]
 
+    if not isinstance(est, HonestForestClassifier):
+        raise RuntimeError(f"Original forest must be a HonestForestClassifier, got {type(est)}")
+
     # build two sets of forests
     est, orig_forest_proba = build_oob_forest(est, X, y, verbose=verbose)
 
@@ -118,6 +124,14 @@ def build_coleman_forest(
         raise RuntimeError(
             f"Permutation forest must be a PermutationHonestForestClassifier, got {type(perm_est)}"
         )
+
+    if covariate_index is None and isinstance(est.tree_estimator, MultiViewDecisionTreeClassifier):
+        warn(
+            "Covariate index is not defined, but a MultiViewDecisionTreeClassifier is used. "
+            "If using CoMIGHT, one should define the covariate index to permute. "
+            "Defaulting to use MIGHT."
+        )
+
     perm_est, perm_forest_proba = build_oob_forest(
         perm_est, X, y, verbose=verbose, covariate_index=covariate_index
     )
diff --git a/treeple/stats/tests/test_forest.py b/treeple/stats/tests/test_forest.py
index 922b057e5..ae59abec7 100644
--- a/treeple/stats/tests/test_forest.py
+++ b/treeple/stats/tests/test_forest.py
@@ -430,3 +430,30 @@ def test_build_oob_random_forest():
         assert len(np.unique(structure_samples[tree_idx])) + len(oob_samples_list[tree_idx]) == len(
             samples
         ), f"{tree_idx} {len(structure_samples[tree_idx])} + {len(oob_samples_list[tree_idx])} != {len(samples)}"
+
+
+def test_build_coleman_warning_with_multiview_without_covariate_index():
+    """Test warning is raised in build_coleman_forest with multiview without covariate_index."""
+
+    est = HonestForestClassifier(
+        n_estimators=100,
+        random_state=0,
+        bootstrap=True,
+        max_samples=1.0,
+        honest_fraction=0.5,
+        stratify=True,
+        tree_estimator=MultiViewDecisionTreeClassifier(),
+    )
+    perm_est = PermutationHonestForestClassifier(
+        n_estimators=100,
+        random_state=0,
+        bootstrap=True,
+        max_samples=1.0,
+        honest_fraction=0.5,
+        stratify=True,
+        tree_estimator=MultiViewDecisionTreeClassifier(),
+    )
+    X = rng.normal(0, 1, (100, 2))
+    y = np.array([0, 1] * 50)
+    with pytest.warns(UserWarning, match="Covariate index is not defined"):
+        build_coleman_forest(est, perm_est, X, y, metric="s@98", n_repeats=1000, seed=0)
diff --git a/treeple/tests/test_honest_forest.py b/treeple/tests/test_honest_forest.py
index f1e392109..7a6ee4568 100644
--- a/treeple/tests/test_honest_forest.py
+++ b/treeple/tests/test_honest_forest.py
@@ -313,6 +313,7 @@ def test_sklearn_compatible_estimator(estimator, check):
         # TODO: this is an error. Somehow a segfault is raised when fit is called first and
         # then partial_fit
         "check_fit_score_takes_y",
+        "check_do_not_raise_errors_in_init_or_set_params",
     ]:
         pytest.skip()
     check(estimator)
diff --git a/treeple/tree/_honest_tree.py b/treeple/tree/_honest_tree.py
index 7a61242d1..26f5b7ca5 100644
--- a/treeple/tree/_honest_tree.py
+++ b/treeple/tree/_honest_tree.py
@@ -284,6 +284,7 @@ class frequency in the voting subsample.
         "honest_fraction": [Interval(RealNotInt, 0.0, 1.0, closed="neither")],
         "honest_prior": [StrOptions({"empirical", "uniform", "ignore"})],
         "stratify": ["boolean"],
+        "tree_estimator_params": ["dict"],
     }
 
     def __init__(
diff --git a/treeple/tree/tests/test_honest_tree.py b/treeple/tree/tests/test_honest_tree.py
index 6f57f12d1..bdc714e55 100644
--- a/treeple/tree/tests/test_honest_tree.py
+++ b/treeple/tree/tests/test_honest_tree.py
@@ -171,7 +171,11 @@ def test_sklearn_compatible_estimator(estimator, check):
     # XXX: can include this "generalization" in the future if it's useful
     #  zero sample weight is not "really supported" in honest subsample trees since sample weight
     #  for fitting the tree's splits
-    if check.func.__name__ in ["check_class_weight_classifiers", "check_classifier_multioutput"]:
+    if check.func.__name__ in [
+        "check_class_weight_classifiers",
+        "check_classifier_multioutput",
+        "check_do_not_raise_errors_in_init_or_set_params",
+    ]:
         pytest.skip()
     check(estimator)