Model Calibration
So far, we have explored various ways to handle the data imbalance. In this chapter, we will see the need to do some post-processing of the prediction scores that we get from the trained models. This can be helpful either during the real-time prediction from the model or during the offline training time evaluation of the model. We will also understand some ways of measuring how calibrated the model is and how imbalanced datasets make the model calibration inevitable.
The following topics will be covered in the chapter:
- Introduction to model calibration
- The influence of data balancing techniques on model calibration
- Plotting calibration curves for a model trained on a real-world dataset
- Model calibration techniques
- The impact of calibration on a model’s performance
By the end of this chapter, you will have a clear understanding of what model calibration means, how to measure it, and when and how to apply it.
