Support more sklearn tags for testing. (#10230)

tests/python/test_with_sklearn.py

@@ -1300,20 +1300,12 @@ def test_estimator_reg(estimator, check):
         ):
             estimator.fit(X, y)
         return
-    if (
-        os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params")
-        != -1
-    ):
-        # A hack to pass the scikit-learn parameter mutation tests.  XGBoost regressor
-        # returns actual internal default values for parameters in `get_params`, but
-        # those are set as `None` in sklearn interface to avoid duplication.  So we fit
-        # a dummy model and obtain the default parameters here for the mutation tests.
-        from sklearn.datasets import make_regression
-
-        X, y = make_regression(n_samples=2, n_features=1)
-        estimator.set_params(**xgb.XGBRegressor().fit(X, y).get_params())
-
-    check(estimator)
+    elif os.environ["PYTEST_CURRENT_TEST"].find("check_regressor_multioutput") != -1:
+        # sklearn requires float64
+        with pytest.raises(AssertionError, match="Got float32"):
+            check(estimator)
+    else:
+        check(estimator)
 
 
 def test_categorical():
@@ -1475,3 +1467,19 @@ def test_fit_none() -> None:
 
     with pytest.raises(ValueError, match="labels"):
         xgb.XGBRegressor().fit(X, None)
+
+
+def test_tags() -> None:
+    for reg in [xgb.XGBRegressor(), xgb.XGBRFRegressor()]:
+        tags = reg._more_tags()
+        assert "non_deterministic" not in tags
+        assert tags["multioutput"] is True
+        assert tags["multioutput_only"] is False
+
+    for clf in [xgb.XGBClassifier()]:
+        tags = clf._more_tags()
+        assert "multioutput" not in tags
+        assert tags["multilabel"] is True
+
+    tags = xgb.XGBRanker()._more_tags()
+    assert "multioutput" not in tags