diff --git a/README.rst b/README.rst
index 62cda8dfb..6a75694ce 100644
--- a/README.rst
+++ b/README.rst
@@ -28,13 +28,13 @@
 .. _Black: :target: https://github.com/psf/black
 
 .. |PythonMinVersion| replace:: 3.10
-.. |NumPyMinVersion| replace:: 1.24.3
-.. |SciPyMinVersion| replace:: 1.10.1
-.. |ScikitLearnMinVersion| replace:: 1.3.2
+.. |NumPyMinVersion| replace:: 1.25.2
+.. |SciPyMinVersion| replace:: 1.11.4
+.. |ScikitLearnMinVersion| replace:: 1.4.2
 .. |MatplotlibMinVersion| replace:: 3.7.3
-.. |PandasMinVersion| replace:: 1.5.3
-.. |TensorflowMinVersion| replace:: 2.13.1
-.. |KerasMinVersion| replace:: 3.0.5
+.. |PandasMinVersion| replace:: 2.0.3
+.. |TensorflowMinVersion| replace:: 2.16.1
+.. |KerasMinVersion| replace:: 3.3.3
 .. |SeabornMinVersion| replace:: 0.12.2
 .. |PytestMinVersion| replace:: 7.2.2
 
diff --git a/doc/install.rst b/doc/install.rst
index 9aace6a2b..6fe66f819 100644
--- a/doc/install.rst
+++ b/doc/install.rst
@@ -8,13 +8,13 @@ Prerequisites
 =============
 
 .. |PythonMinVersion| replace:: 3.10
-.. |NumPyMinVersion| replace:: 1.24.3
-.. |SciPyMinVersion| replace:: 1.10.1
-.. |ScikitLearnMinVersion| replace:: 1.3.2
+.. |NumPyMinVersion| replace:: 1.25.2
+.. |SciPyMinVersion| replace:: 1.11.4
+.. |ScikitLearnMinVersion| replace:: 1.4.2
 .. |MatplotlibMinVersion| replace:: 3.7.3
-.. |PandasMinVersion| replace:: 1.5.3
-.. |TensorflowMinVersion| replace:: 2.13.1
-.. |KerasMinVersion| replace:: 3.0.5
+.. |PandasMinVersion| replace:: 2.0.3
+.. |TensorflowMinVersion| replace:: 2.16.1
+.. |KerasMinVersion| replace:: 3.3.3
 .. |SeabornMinVersion| replace:: 0.12.2
 .. |PytestMinVersion| replace:: 7.2.2
 
diff --git a/doc/whats_new/0.14.rst b/doc/whats_new/0.14.rst
index 2afdbf9bb..5c1751e1a 100644
--- a/doc/whats_new/0.14.rst
+++ b/doc/whats_new/0.14.rst
@@ -1,9 +1,9 @@
 .. _changes_0_14:
 
-Version 0.14.0 (Under development)
-==================================
+Version 0.14.0
+==============
 
-**TBD**
+**August 14, 2025**
 
 Changelog
 ---------
@@ -21,5 +21,8 @@ Enhancements
 Compatibility
 .............
 
+- Compatibility with scikit-learn 1.7
+  :pr:`1137`, :pr:`1145`, :pr:`1146` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Deprecations
 ............
diff --git a/examples/model_selection/plot_instance_hardness_cv.py b/examples/model_selection/plot_instance_hardness_cv.py
index 2990bd6b2..fe7eca6f0 100644
--- a/examples/model_selection/plot_instance_hardness_cv.py
+++ b/examples/model_selection/plot_instance_hardness_cv.py
@@ -28,7 +28,7 @@
 from sklearn.datasets import make_blobs
 
 X, y = make_blobs(n_samples=[950, 50], centers=((-3, 0), (3, 0)), random_state=10)
-plt.scatter(X[:, 0], X[:, 1], c=y)
+_ = plt.scatter(X[:, 0], X[:, 1], c=y)
 
 # %%
 # To introduce instance hardness in our dataset, we add some hard to classify samples:
@@ -36,7 +36,7 @@
     n_samples=10, centers=((3, 0), (-3, 0)), cluster_std=1, random_state=10
 )
 X, y = np.vstack((X, X_hard)), np.hstack((y, y_hard))
-plt.scatter(X[:, 0], X[:, 1], c=y)
+_ = plt.scatter(X[:, 0], X[:, 1], c=y)
 
 # %%
 # Compare cross validation scores using `StratifiedKFold` and `InstanceHardnessCV`
@@ -69,7 +69,7 @@
 results = {}
 for cv in (
     StratifiedKFold(n_splits=5, shuffle=True, random_state=10),
-    InstanceHardnessCV(estimator=LogisticRegression(), n_splits=5, random_state=10),
+    InstanceHardnessCV(estimator=LogisticRegression()),
 ):
     result = cross_validate(
         logistic_regression,
@@ -83,7 +83,7 @@
 
 # %%
 ax = results.plot.box(vert=False, whis=[0, 100])
-ax.set(
+_ = ax.set(
     xlabel="Average precision",
     title="Cross validation scores with different splitters",
     xlim=(0, 1),
diff --git a/imblearn/ensemble/_bagging.py b/imblearn/ensemble/_bagging.py
index 0cf7a4ba8..25e49c538 100644
--- a/imblearn/ensemble/_bagging.py
+++ b/imblearn/ensemble/_bagging.py
@@ -12,15 +12,13 @@
 from sklearn.ensemble import BaggingClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils._param_validation import HasMethods, Interval, StrOptions
-from sklearn.utils.fixes import parse_version
 
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
 from ..utils import Substitution, check_sampling_strategy, check_target_type
 from ..utils._docstring import _n_jobs_docstring, _random_state_docstring
-from ..utils._sklearn_compat import _fit_context, sklearn_version
-from ._common import _bagging_parameter_constraints
+from ..utils._sklearn_compat import _fit_context
 
 
 @Substitution(
@@ -224,11 +222,7 @@ class BalancedBaggingClassifier(BaggingClassifier):
     """
 
     # make a deepcopy to not modify the original dictionary
-    if sklearn_version >= parse_version("1.4"):
-        _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
-    else:
-        _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)
-
+    _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
     _parameter_constraints.update(
         {
             "sampling_strategy": [
@@ -241,9 +235,6 @@ class BalancedBaggingClassifier(BaggingClassifier):
             "sampler": [HasMethods(["fit_resample"]), None],
         }
     )
-    # TODO: remove when minimum supported version of scikit-learn is 1.4
-    if "base_estimator" in _parameter_constraints:
-        del _parameter_constraints["base_estimator"]
 
     def __init__(
         self,
diff --git a/imblearn/ensemble/_common.py b/imblearn/ensemble/_common.py
index 7a78590a3..e7353887d 100644
--- a/imblearn/ensemble/_common.py
+++ b/imblearn/ensemble/_common.py
@@ -19,10 +19,8 @@ def _estimator_has(attr):
     def check(self):
         if hasattr(self, "estimators_"):
             return hasattr(self.estimators_[0], attr)
-        elif self.estimator is not None:
+        else:  # self.estimator is not None
             return hasattr(self.estimator, attr)
-        else:  # TODO(1.4): Remove when the base_estimator deprecation cycle ends
-            return hasattr(self.base_estimator, attr)
 
     return check
 
@@ -45,11 +43,6 @@ def check(self):
     "n_jobs": [None, Integral],
     "random_state": ["random_state"],
     "verbose": ["verbose"],
-    "base_estimator": [
-        HasMethods(["fit", "predict"]),
-        StrOptions({"deprecated"}),
-        None,
-    ],
 }
 
 _adaboost_classifier_parameter_constraints = {
diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 2211f5cc1..afd0cae22 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -5,19 +5,13 @@
 # License: MIT
 
 import copy
-import inspect
 import numbers
 
 import numpy as np
 from sklearn.base import clone
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier
-from sklearn.ensemble._bagging import _parallel_decision_function
-from sklearn.ensemble._base import _partition_estimators
 from sklearn.utils._param_validation import Interval, StrOptions
 from sklearn.utils.fixes import parse_version
-from sklearn.utils.metaestimators import available_if
-from sklearn.utils.parallel import Parallel, delayed
-from sklearn.utils.validation import check_is_fitted
 
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
@@ -28,9 +22,8 @@
     _fit_context,
     get_tags,
     sklearn_version,
-    validate_data,
 )
-from ._common import _bagging_parameter_constraints, _estimator_has
+from ._common import _bagging_parameter_constraints
 
 MAX_INT = np.iinfo(np.int32).max
 
@@ -276,61 +269,6 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         # None.
         return super()._fit(X, y, self.max_samples)
 
-    # TODO: remove when minimum supported version of scikit-learn is 1.1
-    @available_if(_estimator_has("decision_function"))
-    def decision_function(self, X):
-        """Average of the decision functions of the base classifiers.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            The training input samples. Sparse matrices are accepted only if
-            they are supported by the base estimator.
-
-        Returns
-        -------
-        score : ndarray of shape (n_samples, k)
-            The decision function of the input samples. The columns correspond
-            to the classes in sorted order, as they appear in the attribute
-            ``classes_``. Regression and binary classification are special
-            cases with ``k == 1``, otherwise ``k==n_classes``.
-        """
-        check_is_fitted(self)
-
-        # Check data
-        X = validate_data(
-            self,
-            X=X,
-            accept_sparse=["csr", "csc"],
-            dtype=None,
-            ensure_all_finite=(
-                "allow_nan" if get_tags(self).input_tags.allow_nan else True
-            ),
-            reset=False,
-        )
-
-        # Parallel loop
-        n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
-
-        kwargs = {}
-        if "params" in inspect.signature(_parallel_decision_function).parameters:
-            kwargs["params"] = {}
-
-        all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
-            delayed(_parallel_decision_function)(
-                self.estimators_[starts[i] : starts[i + 1]],
-                self.estimators_features_[starts[i] : starts[i + 1]],
-                X,
-                **kwargs,
-            )
-            for i in range(n_jobs)
-        )
-
-        # Reduce
-        decisions = sum(all_decisions) / self.n_estimators
-
-        return decisions
-
     @property
     def base_estimator_(self):
         """Attribute for older sklearn version compatibility."""
diff --git a/imblearn/ensemble/_forest.py b/imblearn/ensemble/_forest.py
index 88b4c514d..a53ad9642 100644
--- a/imblearn/ensemble/_forest.py
+++ b/imblearn/ensemble/_forest.py
@@ -74,12 +74,9 @@ def _local_parallel_build_trees(
         "bootstrap": bootstrap,
     }
 
-    if sklearn_version >= parse_version("1.4"):
-        # TODO: remove when the minimum supported version of scikit-learn will be 1.4
-        # support for missing values
-        params_parallel_build_trees["missing_values_in_feature_mask"] = (
-            missing_values_in_feature_mask
-        )
+    params_parallel_build_trees["missing_values_in_feature_mask"] = (
+        missing_values_in_feature_mask
+    )
 
     tree = _parallel_build_trees(**params_parallel_build_trees)
 
@@ -469,20 +466,9 @@ def __init__(
             "min_impurity_decrease": min_impurity_decrease,
             "ccp_alpha": ccp_alpha,
             "max_samples": max_samples,
+            "monotonic_cst": monotonic_cst,
         }
-        # TODO: remove when the minimum supported version of scikit-learn will be 1.4
-        if sklearn_version >= parse_version("1.4"):
-            # use scikit-learn support for monotonic constraints
-            params_random_forest["monotonic_cst"] = monotonic_cst
-        else:
-            if monotonic_cst is not None:
-                raise ValueError(
-                    "Monotonic constraints are not supported for scikit-learn "
-                    "version < 1.4."
-                )
-            # create an attribute for compatibility with other scikit-learn tools such
-            # as HTML representation.
-            self.monotonic_cst = monotonic_cst
+
         super().__init__(**params_random_forest)
 
         self.sampling_strategy = sampling_strategy
@@ -548,13 +534,6 @@ def fit(self, X, y, sample_weight=None):
         if issparse(y):
             raise ValueError("sparse multilabel-indicator for y is not supported.")
 
-        # TODO: remove when the minimum supported version of scipy will be 1.4
-        # Support for missing values
-        if sklearn_version >= parse_version("1.4"):
-            ensure_all_finite = False
-        else:
-            ensure_all_finite = True
-
         X, y = validate_data(
             self,
             X=X,
@@ -562,23 +541,19 @@ def fit(self, X, y, sample_weight=None):
             multi_output=True,
             accept_sparse="csc",
             dtype=DTYPE,
-            ensure_all_finite=ensure_all_finite,
+            ensure_all_finite=False,
         )
 
-        # TODO: remove when the minimum supported version of scikit-learn will be 1.4
-        if sklearn_version >= parse_version("1.4"):
-            # _compute_missing_values_in_feature_mask checks if X has missing values and
-            # will raise an error if the underlying tree base estimator can't handle
-            # missing values. Only the criterion is required to determine if the tree
-            # supports missing values.
-            estimator = type(self.estimator)(criterion=self.criterion)
-            missing_values_in_feature_mask = (
-                estimator._compute_missing_values_in_feature_mask(
-                    X, estimator_name=self.__class__.__name__
-                )
+        # _compute_missing_values_in_feature_mask checks if X has missing values and
+        # will raise an error if the underlying tree base estimator can't handle
+        # missing values. Only the criterion is required to determine if the tree
+        # supports missing values.
+        estimator = type(self.estimator)(criterion=self.criterion)
+        missing_values_in_feature_mask = (
+            estimator._compute_missing_values_in_feature_mask(
+                X, estimator_name=self.__class__.__name__
             )
-        else:
-            missing_values_in_feature_mask = None
+        )
 
         if sample_weight is not None:
             sample_weight = _check_sample_weight(sample_weight, X)
diff --git a/imblearn/keras/_generator.py b/imblearn/keras/_generator.py
index ce499355f..cf0c944dd 100644
--- a/imblearn/keras/_generator.py
+++ b/imblearn/keras/_generator.py
@@ -20,7 +20,7 @@ def import_from_keras():
             if hasattr(keras.utils, "Sequence"):
                 return (keras.utils.Sequence,), True
             else:
-                return (keras.utils.data_utils.Sequence,), True
+                return (keras.utils.PyDataset,), True
         except ImportError:
             return tuple(), False
 
@@ -31,7 +31,7 @@ def import_from_tensforflow():
             if hasattr(keras.utils, "Sequence"):
                 return (keras.utils.Sequence,), True
             else:
-                return (keras.utils.data_utils.Sequence,), True
+                return (keras.utils.PyDataset,), True
         except ImportError:
             return tuple(), False
 
diff --git a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
index db79fee73..8727fa92f 100644
--- a/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
+++ b/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py
@@ -6,7 +6,6 @@
 # License: MIT
 
 import numbers
-import warnings
 from collections import Counter
 
 import numpy as np
@@ -57,14 +56,6 @@ class CondensedNearestNeighbour(BaseCleaningSampler):
         corresponds to the class labels from which to sample and the values
         are the number of samples to sample.
 
-    estimator_ : estimator object
-        The validated K-nearest neighbor estimator created from `n_neighbors` parameter.
-
-        .. deprecated:: 0.12
-           `estimator_` is deprecated in 0.12 and will be removed in 0.14. Use
-           `estimators_` instead that contains the list of all K-nearest
-           neighbors estimator used for each pair of class.
-
     estimators_ : list of estimator objects of shape (n_resampled_classes - 1,)
         Contains the K-nearest neighbor estimator used for per of classes.
 
@@ -247,18 +238,6 @@ def _fit_resample(self, X, y):
 
         return _safe_indexing(X, idx_under), _safe_indexing(y, idx_under)
 
-    @property
-    def estimator_(self):
-        """Last fitted k-NN estimator."""
-        warnings.warn(
-            (
-                "`estimator_` attribute has been deprecated in 0.12 and will be "
-                "removed in 0.14. Use `estimators_` instead."
-            ),
-            FutureWarning,
-        )
-        return self.estimators_[-1]
-
     def _more_tags(self):
         return {"sample_indices": True}
 
diff --git a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
index 2c2b50c28..d0e40c4b7 100644
--- a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
+++ b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
@@ -5,14 +5,13 @@
 # License: MIT
 
 import numbers
-import warnings
 from collections import Counter
 
 import numpy as np
 from sklearn.base import clone
 from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors
 from sklearn.utils import _safe_indexing
-from sklearn.utils._param_validation import HasMethods, Hidden, Interval, StrOptions
+from sklearn.utils._param_validation import HasMethods, Interval
 
 from ...utils import Substitution
 from ...utils._docstring import _n_jobs_docstring
@@ -48,22 +47,6 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
         :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
         find the nearest-neighbors. By default, it will be a 3-NN.
 
-    kind_sel : {{"all", "mode"}}, default='all'
-        Strategy to use in order to exclude samples in the ENN sampling.
-
-        - If ``'all'``, all neighbours will have to agree with the samples of
-          interest to not be excluded.
-        - If ``'mode'``, the majority vote of the neighbours will be used in
-          order to exclude a sample.
-
-        The strategy `"all"` will be less conservative than `'mode'`. Thus,
-        more samples will be removed when `kind_sel="all"` generally.
-
-        .. deprecated:: 0.12
-           `kind_sel` is deprecated in 0.12 and will be removed in 0.14.
-           Currently the parameter has no effect and corresponds always to the
-           `"all"` strategy.
-
     threshold_cleaning : float, default=0.5
         Threshold used to whether consider a class or not during the cleaning
         after applying ENN. A class will be considered during cleaning when:
@@ -150,7 +133,6 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
             Interval(numbers.Integral, 1, None, closed="left"),
             HasMethods(["kneighbors", "kneighbors_graph"]),
         ],
-        "kind_sel": [StrOptions({"all", "mode"}), Hidden(StrOptions({"deprecated"}))],
         "threshold_cleaning": [Interval(numbers.Real, 0, None, closed="neither")],
         "n_jobs": [numbers.Integral, None],
     }
@@ -161,14 +143,12 @@ def __init__(
         sampling_strategy="auto",
         edited_nearest_neighbours=None,
         n_neighbors=3,
-        kind_sel="deprecated",
         threshold_cleaning=0.5,
         n_jobs=None,
     ):
         super().__init__(sampling_strategy=sampling_strategy)
         self.edited_nearest_neighbours = edited_nearest_neighbours
         self.n_neighbors = n_neighbors
-        self.kind_sel = kind_sel
         self.threshold_cleaning = threshold_cleaning
         self.n_jobs = n_jobs
 
@@ -197,14 +177,6 @@ def _validate_estimator(self):
             self.edited_nearest_neighbours_ = clone(self.edited_nearest_neighbours)
 
     def _fit_resample(self, X, y):
-        if self.kind_sel != "deprecated":
-            warnings.warn(
-                (
-                    "`kind_sel` is deprecated in 0.12 and will be removed in 0.14. "
-                    "It already has not effect and corresponds to the `'all'` option."
-                ),
-                FutureWarning,
-            )
         self._validate_estimator()
         self.edited_nearest_neighbours_.fit_resample(X, y)
         index_not_a1 = self.edited_nearest_neighbours_.sample_indices_
diff --git a/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py b/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py
index b7e6a69e9..fe2b3dde4 100644
--- a/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py
+++ b/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py
@@ -5,7 +5,6 @@
 # License: MIT
 
 import numbers
-import warnings
 from collections import Counter
 
 import numpy as np
@@ -56,14 +55,6 @@ class OneSidedSelection(BaseCleaningSampler):
         corresponds to the class labels from which to sample and the values
         are the number of samples to sample.
 
-    estimator_ : estimator object
-        Validated K-nearest neighbors estimator created from parameter `n_neighbors`.
-
-        .. deprecated:: 0.12
-           `estimator_` is deprecated in 0.12 and will be removed in 0.14. Use
-           `estimators_` instead that contains the list of all K-nearest
-           neighbors estimator used for each pair of class.
-
     estimators_ : list of estimator objects of shape (n_resampled_classes - 1,)
         Contains the K-nearest neighbor estimator used for per of classes.
 
@@ -213,18 +204,6 @@ def _fit_resample(self, X, y):
 
         return X_cleaned, y_cleaned
 
-    @property
-    def estimator_(self):
-        """Last fitted k-NN estimator."""
-        warnings.warn(
-            (
-                "`estimator_` attribute has been deprecated in 0.12 and will be "
-                "removed in 0.14. Use `estimators_` instead."
-            ),
-            FutureWarning,
-        )
-        return self.estimators_[-1]
-
     def _more_tags(self):
         return {"sample_indices": True}
 
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py b/imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py
index 5cc8f4162..21fb47db2 100644
--- a/imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py
+++ b/imblearn/under_sampling/_prototype_selection/tests/test_condensed_nearest_neighbour.py
@@ -117,13 +117,3 @@ def test_condensed_nearest_neighbour_multiclass():
         assert est.classes_[1] in {1, 2, 3}  # other classes
         other_classes.append(est.classes_[1])
     assert len(set(other_classes)) == len(other_classes)
-
-
-# TODO: remove in 0.14
-def test_condensed_nearest_neighbors_deprecation():
-    """Check that we raise a FutureWarning when accessing the parameter `estimator_`."""
-    cnn = CondensedNearestNeighbour(random_state=RND_SEED)
-    cnn.fit_resample(X, Y)
-    warn_msg = "`estimator_` attribute has been deprecated"
-    with pytest.warns(FutureWarning, match=warn_msg):
-        cnn.estimator_
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py b/imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py
index 97a1d0259..8b5f57d1d 100644
--- a/imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py
+++ b/imblearn/under_sampling/_prototype_selection/tests/test_neighbourhood_cleaning_rule.py
@@ -73,12 +73,3 @@ def test_ncr_n_neighbors(data):
 
     # we should have a more aggressive cleaning with n_neighbors is larger
     assert len(sample_indices_3_nn) > len(sample_indices_10_nn)
-
-
-# TODO: remove in 0.14
-@pytest.mark.parametrize("kind_sel", ["all", "mode"])
-def test_ncr_deprecate_kind_sel(data, kind_sel):
-    X, y = data
-
-    with pytest.warns(FutureWarning, match="`kind_sel` is deprecated"):
-        NeighbourhoodCleaningRule(kind_sel=kind_sel).fit_resample(X, y)
diff --git a/imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py b/imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py
index 3fb5458c4..a5e99339a 100644
--- a/imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py
+++ b/imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py
@@ -117,13 +117,3 @@ def test_one_sided_selection_multiclass():
         assert est.classes_[1] in {1, 2, 3}  # other classes
         other_classes.append(est.classes_[1])
     assert len(set(other_classes)) == len(other_classes)
-
-
-# TODO: remove in 0.14
-def test_one_sided_selection_deprecation():
-    """Check that we raise a FutureWarning when accessing the parameter `estimator_`."""
-    oss = OneSidedSelection(random_state=RND_SEED)
-    oss.fit_resample(X, Y)
-    warn_msg = "`estimator_` attribute has been deprecated"
-    with pytest.warns(FutureWarning, match=warn_msg):
-        oss.estimator_
diff --git a/pixi.lock b/pixi.lock
index f2a8a70ef..c922d9fe6 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,22 +9,14 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.8.3-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py310h8deb56e_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.3-py310h3406613_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda
@@ -47,24 +39,19 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.24.3-py310ha4c1d20_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.25.2-py310ha4c1d20_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.8-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-5.0.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.18-hd6af730_0_cpython.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.3.2-py310h1fdf081_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.10.1-py310ha4c1d20_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.4.2-py310h981052a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.4-py310hb13e2d6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-2.0.0-pyh5ca1d4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda
@@ -72,26 +59,16 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda
       - pypi: ./
       osx-64:
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py310h6954a95_3.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.8.3-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/cffi-1.17.1-py310hfce808e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.3-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.3-py310h929a2ac_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-20_osx64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-20.1.8-h3d58e20_1.conda
@@ -106,24 +83,19 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.24.3-py310h7451ae0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.25.2-py310h7451ae0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.2-h6e31bce_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.8-pyhe01879c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.10.1-py310h3900cf1_3.conda
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+      - conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.11.4-py310h3f1db6d_0.conda
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@@ -131,27 +103,17 @@ environments:
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       - pypi: ./
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.1.1-pyhd8ed1ab_0.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2
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@@ -166,24 +128,19 @@ environments:
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@@ -191,65 +148,44 @@ environments:
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-  md5: d1db1b8be7c3a8983dcbbbfe4f0765de
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   depends:
+  - __glibc >=2.17,<3.0.a0
   - libgcc-ng >=12
   - libstdcxx-ng >=12
   constrains:
-  - abseil-cpp =20230125.3
-  - libabseil-static =20230125.3=cxx17*
+  - abseil-cpp =20240116.2
+  - libabseil-static =20240116.2=cxx17*
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1240376
-  timestamp: 1688112986128
+  size: 1264712
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/libabseil-20240116.2-cxx17_hf036a51_1.conda
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-  - libgcc >=13
-  - libstdcxx >=13
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-  - abseil-cpp =20250512.1
-  license: Apache-2.0
-  license_family: Apache
-  purls: []
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-  md5: 1d883cd421a0b0af624c38fa8d043f98
-  depends:
-  - libcxx >=15.0.7
+  - __osx >=10.13
+  - libcxx >=16
   constrains:
-  - libabseil-static =20230125.3=cxx17*
-  - abseil-cpp =20230125.3
+  - abseil-cpp =20240116.2
+  - libabseil-static =20240116.2=cxx17*
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1128875
-  timestamp: 1688113187054
+  size: 1124364
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-  md5: ddf1acaed2276c7eb9d3c76b49699a11
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240116.2-cxx17_h00cdb27_1.conda
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+  md5: f16963d88aed907af8b90878b8d8a05c
   depends:
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-  - libcxx >=18
-  constrains:
-  - abseil-cpp =20250512.1
-  - libabseil-static =20250512.1=cxx17*
-  license: Apache-2.0
-  license_family: Apache
-  purls: []
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-  depends:
-  - libcxx >=15.0.7
+  - __osx >=11.0
+  - libcxx >=16
   constrains:
-  - libabseil-static =20230125.3=cxx17*
-  - abseil-cpp =20230125.3
+  - abseil-cpp =20240116.2
+  - libabseil-static =20240116.2=cxx17*
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1160586
-  timestamp: 1688113314656
+  size: 1136123
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-  - abseil-cpp =20250512.1
-  license: Apache-2.0
-  license_family: Apache
-  purls: []
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@@ -9939,27 +9344,27 @@ packages:
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-  md5: 7af7c59ab24db007dfd82e0a3a343f66
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.62.2-h15f2491_0.conda
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-  - libabseil >=20230125.3,<20230126.0a0
+  - libabseil >=20240116.1,<20240117.0a0
   - libgcc-ng >=12
-  - libprotobuf >=3.21.12,<3.22.0a0
+  - libprotobuf >=4.25.3,<4.25.4.0a0
+  - libre2-11 >=2023.9.1
   - libstdcxx-ng >=12
   - libzlib >=1.2.13,<2.0.0a0
-  - openssl >=3.1.1,<4.0a0
-  - re2 >=2023.3.2,<2023.3.3.0a0
-  - zlib
+  - openssl >=3.2.1,<4.0a0
+  - re2
   constrains:
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+  - grpc-cpp =1.62.2
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 6003265
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-h25350d4_2.conda
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-  md5: aebaded1dc424d96cd651fd59469ff43
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.62.2-h384b2fc_0.conda
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   - libabseil * cxx17*
-  - libabseil >=20230125.3,<20230126.0a0
-  - libcxx >=15.0.7
-  - libprotobuf >=3.21.12,<3.22.0a0
+  - libabseil >=20240116.1,<20240117.0a0
+  - libcxx >=16
+  - libprotobuf >=4.25.3,<4.25.4.0a0
+  - libre2-11 >=2023.9.1
   - libzlib >=1.2.13,<2.0.0a0
-  - openssl >=3.1.1,<4.0a0
-  - re2 >=2023.3.2,<2023.3.3.0a0
-  - zlib
+  - openssl >=3.2.1,<4.0a0
+  - re2
   constrains:
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+  - grpc-cpp =1.62.2
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4196975
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-  md5: 181d9f230758e567c2a5c947ecc5c813
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.62.2-h9c18a4f_0.conda
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-  - libabseil >=20230125.3,<20230126.0a0
-  - libcxx >=15.0.7
-  - libprotobuf >=3.21.12,<3.22.0a0
+  - libabseil >=20240116.1,<20240117.0a0
+  - libcxx >=16
+  - libprotobuf >=4.25.3,<4.25.4.0a0
+  - libre2-11 >=2023.9.1
   - libzlib >=1.2.13,<2.0.0a0
-  - openssl >=3.1.1,<4.0a0
-  - re2 >=2023.3.2,<2023.3.3.0a0
-  - zlib
+  - openssl >=3.2.1,<4.0a0
+  - re2
   constrains:
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+  - grpc-cpp =1.62.2
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4112327
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-h0a426d6_2.conda
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-  md5: e3a7d4ba09b8dc939b98fef55f539220
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.25.3-hd5b35b9_1.conda
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-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
+  - __glibc >=2.17,<3.0.a0
+  - libabseil * cxx17*
+  - libabseil >=20240116.2,<20240117.0a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
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   purls: []
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.conda
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-  md5: 91c01c0d80043917d7c8e1966b9d8449
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libprotobuf-4.25.3-hd4aba4c_1.conda
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+  - libabseil >=20240116.2,<20240117.0a0
+  - libcxx >=17
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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+  - libabseil >=20240116.2,<20240117.0a0
+  - libcxx >=17
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   license: BSD-3-Clause
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   purls: []
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+  - libgcc-ng >=12
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+  license: BSD-3-Clause
+  license_family: BSD
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+  - libabseil * cxx17*
+  - libabseil >=20240116.1,<20240117.0a0
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+  license: BSD-3-Clause
+  license_family: BSD
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-  timestamp: 1727963209370
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda
   sha256: f9b76c2f8a0f96e656843553272e547170182f5b8aba1a6bcba28f7611d87c23
   md5: f9254b5b0193982416b91edcb4b2676f
@@ -19722,22 +18838,6 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 532173
   timestamp: 1745870087418
-- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py310ha8f682b_2.conda
-  sha256: 76bf75ef83e952ef4974e0e6656a7a90b4c4c1c22cea984cb9fc29aca05e5999
-  md5: fdc36a989175bb166109e400c106defa
-  depends:
-  - cffi >=1.11
-  - python >=3.10,<3.11.0a0
-  - python_abi 3.10.* *_cp310
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 435740
-  timestamp: 1745870314659
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
   sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
   md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
diff --git a/pyproject.toml b/pyproject.toml
index ea214c606..4e8e833e8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,10 +25,10 @@ classifiers = [
 ]
 requires-python = ">=3.10"
 dependencies = [
-   "numpy>=1.24.3,<3",
-   "scipy>=1.10.1,<2",
-   "scikit-learn>=1.3.2,<2",
-   "joblib>=1.1.1,<2",
+   "numpy>=1.25.2,<3",
+   "scipy>=1.11.4,<2",
+   "scikit-learn>=1.4.2,<2",
+   "joblib>=1.2.0,<2",
    "threadpoolctl>=2.0.0,<4",
 ]
 
@@ -43,8 +43,8 @@ dev = [
     "jupyterlab",
 ]
 docs = [
-    "pandas>=1.5.3,<3",
-    "tensorflow>=2.13.1,<3",
+    "pandas>=2.0.3,<3",
+    "tensorflow>=2.16.1,<3",
     "matplotlib>=3.7.3,<4",
     "seaborn>=0.12.2,<1",
     "memory_profiler>=0.61.0,<1",
@@ -62,13 +62,13 @@ linters = [
     "pre-commit",
 ]
 optional = [
-    "pandas>=1.5.3,<3",
+    "pandas>=2.0.3,<3",
 ]
 tensorflow = [
-    "tensorflow>=2.13.1,<3",
+    "tensorflow>=2.16.1,<3",
 ]
 keras = [
-    "keras>=3.0.5,<4",
+    "keras>=3.3.3,<4",
 ]
 tests = [
     "packaging>=23.2,<25",
@@ -90,10 +90,10 @@ channels = ["conda-forge"]
 platforms = ["linux-64", "osx-arm64", "osx-64", "win-64"]
 
 [tool.pixi.dependencies]
-numpy = ">=1.24.3,<3"
-scipy = ">=1.10.1,<2"
-scikit-learn = ">=1.3.2,<2"
-joblib = ">=1.1.1,<2"
+numpy = ">=1.25.2,<3"
+scipy = ">=1.11.4,<2"
+scikit-learn = ">=1.4.2,<2"
+joblib = ">=1.2.0,<2"
 threadpoolctl = ">=2.0.0,<4"
 
 [tool.pixi.feature.dev.dependencies]
@@ -124,42 +124,43 @@ ruff = "==0.4.8"
 pre-commit = "*"
 
 [tool.pixi.feature.optional.dependencies]
-pandas = ">=1.5.3,<3"
+pandas = ">=2.0.3,<3"
 
 [tool.pixi.feature.keras]
 platforms = ["linux-64", "osx-arm64", "osx-64"]
 
 [tool.pixi.feature.keras.dependencies]
-keras = ">=3.0.5,<4"
+keras = ">=3.3.3,<4"
 
 [tool.pixi.feature.tensorflow]
 platforms = ["linux-64", "osx-arm64", "osx-64"]
 
 [tool.pixi.feature.tensorflow.dependencies]
-tensorflow = ">=2.13.1,<3"
-keras = ">=3.0.5,<3.9"
+tensorflow = ">=2.16.1,<3"
+keras = ">=3.3.3,<3.9"
 
 [tool.pixi.feature.min-dependencies.dependencies]
-numpy = "==1.24.3"
-scipy = "==1.10.1"
-scikit-learn = "==1.3.2"
-joblib = "==1.1.1"
+numpy = "==1.25.2"
+scipy = "==1.11.4"
+scikit-learn = "==1.4.2"
+joblib = "==1.2.0"
 threadpoolctl = "==2.0.0"
 
 [tool.pixi.feature.min-optional-dependencies.dependencies]
-pandas = "==1.5.3"
+pandas = "==2.0.3"
 
 [tool.pixi.feature.min-keras]
 platforms = ["linux-64", "osx-arm64", "osx-64"]
 
 [tool.pixi.feature.min-keras.dependencies]
-keras = "==3.0.5"
+keras = "==3.3.3"
 
 [tool.pixi.feature.min-tensorflow]
 platforms = ["linux-64", "osx-arm64", "osx-64"]
 
 [tool.pixi.feature.min-tensorflow.dependencies]
-tensorflow = "==2.13.1"
+tensorflow = "==2.16.1"
+keras = "==3.3.3"
 
 [tool.pixi.feature.sklearn-1-4.dependencies]
 scikit-learn = "~=1.4.0"