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

* Add support in sklearn classifier.

python-package/xgboost/sklearn.py

@@ -1215,6 +1215,14 @@ PredtT = TypeVar("PredtT", bound=np.ndarray)
 def _cls_predict_proba(n_classes: int, prediction: PredtT, vstack: Callable) -> PredtT:
     assert len(prediction.shape) <= 2
     if len(prediction.shape) == 2 and prediction.shape[1] == n_classes:
+        # multi-class
+        return prediction
+    if (
+        len(prediction.shape) == 2
+        and n_classes == 2
+        and prediction.shape[1] >= n_classes
+    ):
+        # multi-label
         return prediction
     # binary logistic function
     classone_probs = prediction
@@ -1374,9 +1382,13 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
             # If output_margin is active, simply return the scores
             return class_probs
 
-        if len(class_probs.shape) > 1:
-            # turns softprob into softmax
+        if len(class_probs.shape) > 1 and self.n_classes_ != 2:
+            # multi-class, turns softprob into softmax
             column_indexes: np.ndarray = np.argmax(class_probs, axis=1)  # type: ignore
+        elif len(class_probs.shape) > 1 and class_probs.shape[1] != 1:
+            # multi-label
+            column_indexes = np.zeros(class_probs.shape)
+            column_indexes[class_probs > 0.5] = 1
         else:
             # turns soft logit into class label
             column_indexes = np.repeat(0, class_probs.shape[0])