Calibrationsplots

Calibrationsplots are graphical representations used to assess the calibration of a probabilistic model. They help visualize how well the predicted probabilities of an event align with its actual observed frequencies. The most common type of calibrationsplot is the reliability diagram.

A reliability diagram plots the predicted probability on the x-axis against the observed frequency of the event on the y-axis. For a perfectly calibrated model, the points would lie along the diagonal line, often referred to as the "perfect calibration line" or "line of equality." Deviations from this line indicate areas where the model is over-predicting or under-predicting the probability of the event. For instance, if the model predicts an event with 80% probability, and it actually occurs 80% of the time, the point would be on the diagonal. If it occurs only 60% of the time, the point would lie below the diagonal.

Beyond reliability diagrams, other calibrationsplots might include:

Expected calibration error (ECE) plots, which summarize the miscalibration across different probability bins.

Impact of calibration plots, which assess how calibration affects downstream tasks or metrics.

These plots are crucial in machine learning, particularly for classification tasks where confidence scores or probabilities are generated. They are essential for understanding the trustworthiness of a model's predictions and for selecting appropriate models when reliable probability estimates are required, such as in medical diagnosis or risk assessment. A well-calibrated model's predicted probabilities can be directly interpreted as the likelihood of the event occurring.