👉 The calibration project is a critical phase in the development of machine learning models, particularly in scenarios where the model's performance needs to be fine-tuned to align with real-world data distributions. This involves adjusting the model's parameters so that its predictions match known ground truths or benchmarks, often through iterative processes like cross-validation. The goal is to ensure that the model not only performs well on training data but also generalizes effectively to new, unseen data, thereby improving its reliability and accuracy in practical applications. Calibration typically includes techniques such as Platt scaling or isotonic regression to adjust the model's output probabilities, making them more interpretable and reliable.
