Probability calibration is the process of adjusting the probabilities output by a machine learning model to make them more accurate. It involves training a second model to calibrate the probabilities of a given model, making the probabilities more reliable for the given data.
The CalibratedClassifierCV class in scikit-learn is used to calibrate the probabilities output by a machine learning model. It fits a given model to the data, and then applies a calibration function to the probabilities, making them more accurate and reliable.