Calibration in machine learning refers to how well a model's predicted probabilities of outcomes reflect the true probabilities observed in reality. A classifier is well-calibrated if, for example, among all instances to which it assigns a 70% probability of being positive, about 70% of those instances are actually positive. This concept is important especially for probabilistic classifiers: beyond accuracy, we care that a predicted confidence of 0.9 truly signifies roughly a 90% chance of the event. Poor calibration can manifest as over-confidence or under-confidence, meaning the predicted probabilities are not aligned with outcomes. In practice, calibration is often evaluated with calibration curves or reliability diagrams and quantified by metrics like the Brier score or expected calibration error. Techniques such as Platt scaling (sigmoid calibration) or isotonic regression can be applied after training to adjust a model's output probabilities for better alignment with observed frequencies. These calibration methods learn a mapping from the classifier's raw scores to calibrated probabilities, usually using a hold-out validation set. Calibration is crucial in applications like medical diagnosis or weather forecasting where decision-making relies on trustworthy confidence estimates.