👉 Calibration computing is a process used to fine-tune machine learning models, particularly in the context of probabilistic predictions. It involves adjusting the model's confidence estimates so that the predicted probabilities closely match the actual observed frequencies in the data. The goal is to ensure that when a model predicts a probability of 0.8 for an event occurring, about 80% of the time that event actually happens. This is achieved by analyzing the model's outputs, often through techniques like temperature scaling or Platt scaling, which modify the raw output scores to produce more realistic probability distributions. Proper calibration is crucial for applications where decision-making relies on the model's confidence levels, such as in medical diagnosis or autonomous driving systems.
