Implement __sklearn_is_fitted__. (#7230)

python-package/xgboost/sklearn.py

@@ -435,6 +435,9 @@ class XGBModel(XGBModelBase):
         '''Tags used for scikit-learn data validation.'''
         return {'allow_nan': True, 'no_validation': True}
 
+    def __sklearn_is_fitted__(self) -> bool:
+        return hasattr(self, "_Booster")
+
     def get_booster(self) -> Booster:
         """Get the underlying xgboost Booster of this model.
 
@@ -444,7 +447,7 @@ class XGBModel(XGBModelBase):
         -------
         booster : a xgboost booster of underlying model
         """
-        if not hasattr(self, '_Booster'):
+        if not self.__sklearn_is_fitted__():
             from sklearn.exceptions import NotFittedError
             raise NotFittedError('need to call fit or load_model beforehand')
         return self._Booster

tests/python-gpu/test_gpu_with_sklearn.py

@@ -19,7 +19,7 @@ def test_gpu_binary_classification():
     from sklearn.datasets import load_digits
     from sklearn.model_selection import KFold
 
-    digits = load_digits(2)
+    digits = load_digits(n_class=2)
     y = digits['target']
     X = digits['data']
     kf = KFold(n_splits=2, shuffle=True, random_state=rng)