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earn how to convert classifier scores into true class probabilities to give you more confidence in your predictions
In today's data-driven world, the accuracy of predictive models is increasingly valued, and one of the key issues is how to convert the classifier's scores into true category probabilities. These probabilities are not only a reflection of the prediction results, but also a key indicator for evaluating the reliability of the model.
“If a forecaster assigns a probability of 30 to an event, then in the long run, the actual probability of its occurrence should be close to 30%.”
In classification problems, model calibration is an important step to improve prediction reliability. Even if a classifier does a good job of separating the classes, its predicted probabilities may be far from the true reality. Therefore, performing a calibration can help improve these estimates.
Many evaluation metrics have been proposed to measure how calibrated the probabilities produced by a classifier are. Examples of foundational work include Expected Calibration Error (ECE). It is worth noting that in the 2020s, indicators such as Adaptive Calibration Error (ACE) and Test-based Calibration Error (TCE) have emerged to address the problem of ECE in high concentration. limitations that may arise in certain circumstances.
Among these advances, the Estimated Calibration Index (ECI) is one of the major breakthroughs of the 2020s. It expands the concept of ECE and provides a more detailed measurement for model calibration, especially for model overconfidence. or insufficient situations. Originally developed for a binary setting, the ECI was subsequently adapted to the multi-class setting as well, providing local and global insights into model calibration."Through a series of experiments, Famiglini et al. demonstrate the effectiveness of the framework in providing a more accurate understanding of the model's calibration level and discuss strategies to reduce bias in calibration estimates."
In addition to the basic calibration methods, there are some specialized univariate calibration methods that can be used to convert classifier scores into class probabilities for two types of cases, including assignment value method, Bayesian method, equal interval regression, Platt scaling and Bayesian Binning to Quantification (BBQ) calibration, among others.
In the field of probabilistic forecasting and prediction, one of the commonly used evaluation tools is the Brier score, which is used to measure the predictive accuracy of a set of forecasts, that is, whether the magnitude of the assigned probabilities matches the relative frequency of the observed outcomes. This is different from accuracy and precision. As Daniel Kahneman puts it, "If you assign a probability of 0.6 to all events that occur and a probability of 0.4 to all events that do not occur, your calibration is perfect." But your ability to recognize it is terrible. ”
In regression analysis, the calibration problem refers to how to use known data to predict another variable. This kind of backward regression is sometimes called slice backward regression. For the multi-class case, appropriate multivariate calibration methods are needed to convert classifier scores into class probabilities.
"For example, using tree rings or radiocarbon dating of objects is a good example of how we can model the relationship between known ages and observations."
However, whether the model should focus on minimizing observation error or date error when relating known ages to observations will produce different results, especially when extrapolating. Will increase with distance from known results.
Taking all of the above into account, model calibration can not only improve the accuracy of predictions, but also enhance users' confidence in the results. In the increasingly automated decision-making process, how to effectively convert the model's scores into true category probabilities becomes an important topic for future research. Faced with these strategies and methods, readers can't help but think: When examining the accuracy of model predictions, what indicators or steps should we focus on to ensure the credibility of the model?
