There are many techniques to improve the performance of an AI model, out of these model calibration is one of the most efficient ways to attain high confidence in machine learning models by improving both model accuracy and fairness.
Model calibration is a post processing technique to improvise probability estimates of an AI model. The objective is to improve the model in such a manner that distribution and behaviours of predicted and observed probability matches. In other words, we determine whether the scores generated by a model can be perceived as probabilities. A model which is poorly calibrated is prone to hamper the performance and generate unsound results. Assume a scenario of encountering a storm when the probability of rain displayed in the weather application is less than 5%, this happens when a model is not well calibrated.
Which is better 95% accuracy with 0.94 confidence or 95% accuracy with 0.99 confidence?
A model can be under confident or overconfident when it is not well calibrated. We are then required to match these probability scores to the true likelihood of the events efficiently. As depicted in the illustration- both models have 95% accuracy, one with 0.94 confidence and another with 0.99 confidence. The question now is which model is better in terms of performance. Here model 1 is the right choice as it thinks of being correct 94% of the time and is indeed correct about 95% of the time. On the other hand, model 2 is overconfident as it presumes to be correct about 99% of the time but is actually correct about 95% of the time for each prediction. We can not go after overconfident models for high risk applications for e.g, determining the probability of a person being ill.
Performing calibration can affect model accuracy and in general it is observed that calibrated models tend to be a bit less accurate than uncalibrated ones. But this distinction is significantly low, and the advantages it offers are typically more significant. A well calibrated model has a high possibility of succeeding by generating accurate results.
Significance of model calibration
- Mission critical applications: Model calibration is required for mission critical applications where likelihood of a data point associated with class is quite prominent for instance, building a model to predict likelihood of an individual being covid positive. Similarly, operational calibration is performed when there is a need to calibrate confidence function to mitigate inaccurate high confidence like in case of DNN models.
- Complex ML systems: Calibration is advantageous in complex ML systems and real world scenarios. It modifies the result of ML models after being trained and preserves monotonicity of the output. Calibrating the model is a crucial step to improve prediction performance if the objective is to obtain good probability prediction.
- Disparity in training dataset: A biased training dataset is normally inclined to generate a high probability score for the majority class. In such scenarios, models focus more on the majority class instead of giving equal weightage to all groups. This disparity can be mitigated with the help of calibration making a model to generate accurate and fair predictions.
Points to ponder while calibrating
- Sampling of training data: Mis-calibration is a common problem for ML models that are not trained utilising a probabilistic framework and where there is bias-ness in the training data. Changing distribution of positive and negative examples and going for harder or simpler examples while training should also be taken under consideration.
- Congenital characteristic of a model: In most of the scenarios the congenital characteristic of a model is responsible to determine whether a model ends up being calibrated or not. Like in case of logistic regression, no additional post training is required. For Gaussian Naive Bayes, the probabilities are nudged near 0 or 1 by virtue of their fundamental suppositions regarding feature independence. Nonetheless, in the case of random forest classifier values near 0 or 1 are obtained rarely and the only sure shot way to accomplish 0 or 1 is when each model returns value near 0 or 1 which is an intriguing occasion from a probabilistic viewpoint.
- Weights learned from training the base model are often required to be frozen before the calibration layer.
- Calibration model significantly changes the output distributions yet launching calibrated model as it is can create major disruptions. Therefore investing more time in the experiment cycle for tuning can be seen as a plausible solution.
Model calibration is onerous, time consuming and a wearing task. A model can be under confident or overconfident while making predictions therefore, various steps are required to be taken into consideration to evaluate and improve confidence measure accuracy efficiently. But it is an important task if you want to build a reliable and robust service.
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Authored by: Vrinda Tandon