Deprecate use_label_encoder in XGBClassifier. (#7822)

* Deprecate `use_label_encoder` in XGBClassifier.

* We have removed the encoder, now prepare to remove the indicator.

demo/guide-python/cat_in_the_dat.py

@@ -63,7 +63,6 @@ def load_cat_in_the_dat() -> tuple[pd.DataFrame, pd.Series]:
 
 params = {
     "tree_method": "gpu_hist",
-    "use_label_encoder": False,
     "n_estimators": 32,
     "colsample_bylevel": 0.7,
 }

demo/guide-python/continuation.py

@@ -14,13 +14,13 @@ def training_continuation(tmpdir: str, use_pickle: bool) -> None:
     """Basic training continuation."""
     # Train 128 iterations in 1 session
     X, y = load_breast_cancer(return_X_y=True)
-    clf = xgboost.XGBClassifier(n_estimators=128, use_label_encoder=False)
+    clf = xgboost.XGBClassifier(n_estimators=128)
     clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
     print("Total boosted rounds:", clf.get_booster().num_boosted_rounds())
 
     # Train 128 iterations in 2 sessions, with the first one runs for 32 iterations and
     # the second one runs for 96 iterations
-    clf = xgboost.XGBClassifier(n_estimators=32, use_label_encoder=False)
+    clf = xgboost.XGBClassifier(n_estimators=32)
     clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
     assert clf.get_booster().num_boosted_rounds() == 32
 
@@ -54,14 +54,14 @@ def training_continuation_early_stop(tmpdir: str, use_pickle: bool) -> None:
     n_estimators = 512
 
     X, y = load_breast_cancer(return_X_y=True)
-    clf = xgboost.XGBClassifier(n_estimators=n_estimators, use_label_encoder=False)
+    clf = xgboost.XGBClassifier(n_estimators=n_estimators)
     clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss", callbacks=[early_stop])
     print("Total boosted rounds:", clf.get_booster().num_boosted_rounds())
     best = clf.best_iteration
 
     # Train 512 iterations in 2 sessions, with the first one runs for 128 iterations and
     # the second one runs until early stop.
-    clf = xgboost.XGBClassifier(n_estimators=128, use_label_encoder=False)
+    clf = xgboost.XGBClassifier(n_estimators=128)
     # Reinitialize the early stop callback
     early_stop = xgboost.callback.EarlyStopping(
         rounds=early_stopping_rounds, save_best=True
@@ -79,15 +79,13 @@ def training_continuation_early_stop(tmpdir: str, use_pickle: bool) -> None:
     else:
         path = os.path.join(tmpdir, "model-first-128.json")
         clf.save_model(path)
-        loaded = xgboost.XGBClassifier(use_label_encoder=False)
+        loaded = xgboost.XGBClassifier()
         loaded.load_model(path)
 
     early_stop = xgboost.callback.EarlyStopping(
         rounds=early_stopping_rounds, save_best=True
     )
-    clf = xgboost.XGBClassifier(
-        n_estimators=n_estimators - 128, use_label_encoder=False
-    )
+    clf = xgboost.XGBClassifier(n_estimators=n_estimators - 128)
     clf.fit(
         X,
         y,

demo/guide-python/predict_first_ntree.py

@@ -35,7 +35,7 @@ def native_interface():
 def sklearn_interface():
     X_train, y_train = load_svmlight_file(train)
     X_test, y_test = load_svmlight_file(test)
-    clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1, use_label_encoder=False)
+    clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1)
     clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
     assert clf.n_classes_ == 2