Introduction To Conformal Prediction With Python

Summary

A prerequisite for trust in machine learning is uncertainty quantification. Without it, an accurate prediction and a wild guess look the same.

Yet many machine learning models come without uncertainty quantification. And while there are many approaches to uncertainty – from Bayesian posteriors to bootstrapping – we have no guarantees that these approaches will perform well on new data.

“I really enjoyed reading the book. The data science and machine learning community needs more people like Christoph Molnar who are able to translate emerging breakthrough research into digestible concepts. I can see this book becoming a key piece in accelerating the rate of adoption of conformal ML.”
– Guilherme Del Nero Maia, Principal Data Science at Jabil

# Computer Neural Networks