Question: I am trying to run XGboost with with calibrated classifier, below is the snippet of code where I am facing the error: from sklearn.calibration import CalibratedClassifierCV from xgboost import XGBClassifier import numpy as np x_train =np.array([1,2,2,3,4,5,6,3,4,10,]).reshape(-1,1) y_train = np.array([1,1,1,1,1,3,3,3,3,3]) x_cfl=XGBClassifier(n_estimators=1) x_cfl.fit(x_train,y_train) sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid") sig_clf.fit(x_train, y_train)  Error: TypeError: predict_proba() got an unexpected keyword argument 'X'"  Full Trace: TypeError Traceback (most recent call last) <ipython-input-48-08dd0b4ae8aa> in <module> ----> 1 sig_clf.fit(x_train, y_train) ~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in fit(self, X, y, sample_weight) 309 parallel = Parallel(n_jobs=self.n_jobs) 310 --> 311 self.calibrated_classifiers_ = parallel( 312 delayed(_fit_classifier_calibrator_pair)( 313 clone(base_estimator), X, y, train=train, test=test, ~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self, iterable) 1039 # remaining jobs. 1040 self._iterating = False -> 1041 if self.dispatch_one_batch(iterator): 1042 self._iterating = self._original_iterator is not None 1043 ~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator) 857 return False 858 else: --> 859 self._dispatch(tasks) 860 return True 861 ~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in _dispatch(self, batch) 775 with self._lock: 776 job_idx = len(self._jobs) --> 777 job = self._backend.apply_async(batch, callback=cb) 778 # A job can complete so quickly than its callback is 779 # called before we get here, causing self._jobs to ~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback) 206 def apply_async(self, func, callback=None): 207 """Schedule a func to be run""" --> 208 result = ImmediateResult(func) 209 if callback: 210 callback(result) ~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py in __init__(self, batch) 570 # Don't delay the application, to avoid keeping the input 571 # arguments in memory --> 572 self.results = batch() 573 574 def get(self): ~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self) 260 # change the default number of processes to -1 261 with parallel_backend(self._backend, n_jobs=self._n_jobs): --> 262 return [func(*args, **kwargs) 263 for func, args, kwargs in self.items] 264 ~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in <listcomp>(.0) 260 # change the default number of processes to -1 261 with parallel_backend(self._backend, n_jobs=self._n_jobs): --> 262 return [func(*args, **kwargs) 263 for func, args, kwargs in self.items] 264 ~/anaconda3/lib/python3.8/site-packages/sklearn/utils/fixes.py in __call__(self, *args, **kwargs) 220 def __call__(self, *args, **kwargs): 221 with config_context(**self.config): --> 222 return self.function(*args, **kwargs) ~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in _fit_classifier_calibrator_pair(estimator, X, y, train, test, supports_sw, method, classes, sample_weight) 443 n_classes = len(classes) 444 pred_method = _get_prediction_method(estimator) --> 445 predictions = _compute_predictions(pred_method, X[test], n_classes) 446 447 sw = None if sample_weight is None else sample_weight[test] ~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in _compute_predictions(pred_method, X, n_classes) 499 (X.shape[0], 1). 500 """ --> 501 predictions = pred_method(X=X) 502 if hasattr(pred_method, '__name__'): 503 method_name = pred_method.__name__ TypeError: predict_proba() got an unexpected keyword argument 'X'  I am quite surprised by this, as it was running for me till yesterday, same code is running when I use some other Classifier. from sklearn.calibration import CalibratedClassifierCV from xgboost import XGBClassifier import numpy as np x_train = np.array([1,2,2,3,4,5,6,3,4,10,]).reshape(-1,1) y_train = np.array([1,1,1,1,1,3,3,3,3,3]) x_cfl=LGBMClassifier(n_estimators=1) x_cfl.fit(x_train,y_train) sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid") sig_clf.fit(x_train, y_train)  Output: CalibratedClassifierCV(base_estimator=LGBMClassifier(n_estimators=1))  Is there a problem with my Xgboost installation?? I use conda for installation and last I remember I had uninstalled xgboost yesterday and installed it again. my xgboost version:  1.3.0 

Answer: I believe that the problem comes from XGBoost. It's explained here: https://github.com/dmlc/xgboost/pull/6555 XGBoost defined: predict_proba(self, data, ... instead of: predict_proba(self, X, ... And since sklearn 0.24 calls clf.predict_proba(X=X), an exception is thrown. Here is an idea to fix the problem without changing the version of your packages: Create a class that inherits XGBoostClassifier to override predict_proba with the right argument names and call super().