Initial support for multi-label classification. (#7521)

* Add support in sklearn classifier.

tests/python/test_with_sklearn.py

@@ -1194,6 +1194,24 @@ def test_estimator_type():
         cls.load_model(path)  # no error
 
 
+def test_multilabel_classification() -> None:
+    from sklearn.datasets import make_multilabel_classification
+
+    X, y = make_multilabel_classification(
+        n_samples=32, n_classes=5, n_labels=3, random_state=0
+    )
+    clf = xgb.XGBClassifier(tree_method="hist")
+    clf.fit(X, y)
+    booster = clf.get_booster()
+    learner = json.loads(booster.save_config())["learner"]
+    assert int(learner["learner_model_param"]["num_target"]) == 5
+
+    np.testing.assert_allclose(clf.predict(X), y)
+    predt = (clf.predict_proba(X) > 0.5).astype(np.int64)
+    np.testing.assert_allclose(clf.predict(X), predt)
+    assert predt.dtype == np.int64
+
+
 def run_data_initialization(DMatrix, model, X, y):
     """Assert that we don't create duplicated DMatrix."""