A simple implementation of two methods of calibrating machine learning models - Platt scaling and isotonic regression. For the main notebook with a demo of the calibration on MNIST dataset, see Notebook.ipynb. The modules used are contained in model.py, calibration.py, and plotting.py.
For a short description of fitting a logistic regression line using linear regression, see Sigmoid regression.ipynb.