Detect and Correct Label Shift with BBSE in Python

Your classifier can stay accurate but its probabilities go wrong when class mix changes — learn BBSE (Black Box Shift Estimation) to detect label shift and correct class priors. Follow a lightweight pipeline to spot shift from unlabeled production predictions, estimate new priors via a linear solve, and rescale probabilities without retraining to restore calibration and reliable thresholds. Demo uses NumPy and scikit-learn (DecisionTreeClassifier, predict_proba) to show confusion matrices, np.linalg.solve, and probability reweighting. Subscribe for more AI foundations in Python. #MachineLearning #AI #Python #LabelShift #ModelMonitoring #DataScience #Tutorial