Answer / Shailesh Kumar

Calibration in machine learning refers to the process of adjusting probabilistic output of a classification model to match the true expected frequency of each class in the data. It ensures that the predicted probability of an instance belonging to a certain class matches the actual proportion of instances from that class in the dataset. Calibration helps to improve the reliability and interpretability of a model's predictions.

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