Neural Network Calibration for CTR Prediction

After a machine learning model has been trained, calibration techniques correct its prediction errors to produce predictions that are more robust and confident. As calibration techniques, we implement isotonic regression, Platt's scaling, neural networks, spline regression, and temperature scaling and test them on the prediction of click-through rate (CTR), which is an unbalanced task. We use 3 neural network based CTR prediction models on a publicly available CTR dataset and measure the improvements. Our findings show that isotonic regression is the fastest method whereas isotonic regression and spline regression are the two techniques that improves the performance the most.