The goal of this issue is to track the support of estimator.fit(X, y, sample_weight=..) throughout the library. As of scikit-learn version 1.8:

• sklearn.cluster.AffinityPropagation
• sklearn.cluster.AgglomerativeClustering
• sklearn.cluster.Birch
• sklearn.cluster.FeatureAgglomeration
• sklearn.cluster.HDBSCAN
• sklearn.cluster.MeanShift
• sklearn.cluster.OPTICS
• sklearn.cluster.SpectralClustering
• sklearn.compose.TransformedTargetRegressor
• sklearn.cross_decomposition.CCA
• sklearn.cross_decomposition.PLSCanonical
• sklearn.cross_decomposition.PLSRegression
• sklearn.discriminant_analysis.LinearDiscriminantAnalysis
• sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
• sklearn.ensemble.HistGradientBoostingClassifier and HistGradientBoostingRegressor binning (added manually)
  Added sample weight handling to BinMapper under HGBT #29641
• sklearn.ensemble.StackingClassifier
• sklearn.ensemble.StackingRegressor
• sklearn.ensemble.VotingClassifier
• sklearn.ensemble.VotingRegressor
• sklearn.gaussian_process.GaussianProcessClassifier
• sklearn.gaussian_process.GaussianProcessRegressor, A possible alternative to alpha at fit-time for GaussianProcessRegressor #7975, FEA Add sample variance support for GaussianProcessRegressor.fit #24188
• sklearn.linear_model.ARDRegression
• sklearn.linear_model.Lars
• sklearn.linear_model.LarsCV
• sklearn.linear_model.LassoLars
• sklearn.linear_model.LassoLarsCV
• sklearn.linear_model.LassoLarsIC
• sklearn.linear_model.MultiTaskElasticNet
  ENH add sparse and sample_weight support to MultiTaskElasticNet #33440
• sklearn.linear_model.MultiTaskElasticNetCV
  ENH add sparse and sample_weight support to MultiTaskElasticNet #33440
• sklearn.linear_model.MultiTaskLasso
  ENH add sparse and sample_weight support to MultiTaskElasticNet #33440
• sklearn.linear_model.MultiTaskLassoCV
  ENH add sparse and sample_weight support to MultiTaskElasticNet #33440
• sklearn.linear_model.OrthogonalMatchingPursuit
• sklearn.linear_model.OrthogonalMatchingPursuitCV
• sklearn.linear_model.PassiveAggressiveClassifier
  deprecated
• sklearn.linear_model.PassiveAggressiveRegressor
  deprecated
• sklearn.linear_model.TheilSenRegressor
• sklearn.model_selection.FixedThresholdClassifier
• sklearn.model_selection.TunedThresholdClassifierCV
• sklearn.multiclass.OneVsOneClassifier
• sklearn.multiclass.OneVsRestClassifier
• sklearn.multioutput.ClassifierChain
• sklearn.multiclass.OutputCodeClassifier
• sklearn.multioutput.RegressorChain
• sklearn.neighbors.KNeighborsClassifier
• sklearn.neighbors.KNeighborsRegressor
• sklearn.neighbors.NearestCentroid
• sklearn.neighbors.RadiusNeighborsClassifier
• sklearn.neighbors.RadiusNeighborsRegressor
• sklearn.semi_supervised.LabelPropagation
• sklearn.semi_supervised.LabelSpreading
• sklearn.semi_supervised.SelfTrainingClassifier

Code to generate that list

from inspect import signature
import sklearn
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instances

[est for name, est in all_estimators(type_filter=["classifier", "regressor", "cluster"]) if "sample_weig
      ⋮ ht" not in signature(next(_construct_instances(est)).fit).parameters]