What is Model Calibration?
Calibration of a machine learning model involves making little but meaningful changes to the model’s predictions in order to improve both accuracy and confidence in those predictions. Specifically, the goal of model calibration is to make that the model’s anticipated probabilities are consistent with reality.
Several popular machine learning models, such as logistic regression and support vector machines, are built to provide probability estimates, which necessitates the requirement for ML model calibration. It’s possible, however, that these probability estimates aren’t calibrated very well, in which case they don’t represent reality.
When to calibrate models?
When making decisions based on probability estimates or gauging the effectiveness of a model, calibration is essential. Logistic regression, support vector machines, and neural networks are all examples of machine learning models that provide probability estimates and, thus, benefit from calibration.
- Decision-making based on class probability in classification tasks In the context of medical diagnosis, for instance, the anticipated probability of a condition is used to determine whether or not more testing or treatment is warranted.
- Risk assessment tasks In the field of finance, for instance, the estimated probability of an increase or decrease in stock price might be used to guide investment choices.
- Analyzing how various models fare To establish reliable comparisons between the efficacy of various models, it is crucial to first guarantee that the projected probabilities are properly calibrated.
- When training a model on an unbalanced dataset Calibration issues are a common problem for models trained on unbalanced datasets, particularly if the model is skewed toward the majority class.
Model calibration should be regarded as a routine step in creating and deploying machine learning models anytime probability estimates are needed in making decisions or gauging performance.
Methods to Calibrate Models
Machine learning models may be calibrated in a number of ways. There are typically three approaches:
- Histogram binning– Partitioning the projected probabilities into bins and calculating the average probability for each bin is histogram binning, a straightforward approach to calibrating models. In this case, the calibrated probability estimate is the mean probability.
- Platt scaling– When calibrating models for binary classification, Platt scaling is often used. It entails utilizing a different calibration dataset to fit a logistic regression model to the original model’s output.
- Isotonic regression– In contrast to parametric model calibration techniques, isotonic regression does not assume anything about the distribution of the projected probabilities. Predicted probabilities are used to fit a monotonic function based on a different calibration dataset. This function converts estimates of the probability to estimates that are more in line with reality.
Note that ML model calibration may also be accomplished using cross-validation, in which the data is partitioned into a number of “folds.” With the calibration function being fit to the “training” folds and the “validation” folds serving as an evaluation. This method has the potential to improve model performance estimation by lowering the likelihood of overfitting.
Keep in mind that models that provide a probability for multiple classes need a different method of calibration than binary classification models, for which logistic regression or isotonic regression may be used to adjust the projected probabilities. Curve calibration is a method for calibrating machine learning models that output probability estimates for multiple classes.
In general, the needs of the model and the context in which it will be used will determine which calibration method is used. To confirm the calibration has improved the model’s accuracy and dependability, it must be tested on a distinct dataset.
Inaccurate conclusions and predictions might result from using a machine learning model that hasn’t been properly calibrated. Overconfidence and underestimation of danger may result, for instance, if a model produces probabilities that are continuously excessively high. The risk of missing opportunities and underestimating possible rewards increases if the model routinely produces probabilities that are too low.
Model calibration is a crucial procedure in the creation and release of machine learning models because it improves their accuracy, reliability, and trustworthiness.