A deep dive into advanced evaluation for data scientists, discussing why accuracy is often misleading and exploring alternative metrics for classification and regression tasks like ROC-AUC, Log Loss, R², RMSLE, and Quantile Loss.
A hands-on tutorial in Python for sensor engineers on Bayesian sensor calibration, which combines statistical models and data to optimally calibrate sensors. This technique is crucial in engineering to minimize sensor measurement uncertainty. The tutorial provides Python code to perform such calibration numerically using existing libraries.
