Summary
In this chapter, we went through the basic concepts of model calibration, why we should care about it, how to measure whether a model is calibrated, how data imbalance affects the model calibration, and, finally, how to calibrate an uncalibrated model. Some of the calibration techniques we talked about include Platt’s scaling, isotonic regression, temperature scaling, and label smoothing.
With this, we come to the end of this book. Thank you for dedicating your time to reading the book. We trust that it has broadened your knowledge of handling imbalanced datasets and their practical applications in machine learning. As we draw this book to a close, we’d like to offer some concluding advice on how to effectively utilize the techniques discussed.
Like other machine learning techniques, the methods discussed in this book can be highly useful under the right conditions, but they also come with their own set of challenges. Recognizing when and where to apply...
