Answer / Pramod Kumar

In supervised learning, calibration is the process of adjusting probabilistic outputs (e.g., predicted probabilities) to match the true outcomes more closely. There are two primary methods for calibration: 1) Platt's Scaled Binary Probability Estimator (for binary classifiers) and 2) Isotonic Regression (for multi-class classifiers).

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