Probability calibration is the process of adjusting the predicted probabilities of a model to better reflect the actual outcomes. For example, if a weather forecast predicts a 70% chance of rain, probability calibration ensures that it actually rains 70% of the time when such a prediction is made. This helps improve the reliability of predictions in various fields, including machine learning and statistics. Calibration techniques can include methods like Platt scaling and isotonic regression, which modify the predicted probabilities based on historical data. By ensuring that predicted probabilities align with real-world frequencies, probability calibration enhances decision-making and risk assessment in applications such as finance, healthcare, and meteorology.