Introduction To Conformal Prediction With Python 1st Edition by Christoph Molnar ISBN B0BW2X919P 979-8377509356

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ISBN 10: B0BW2X919P
ISBN 13: 979-8377509356
Author: Christoph Molnar

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

At first glance conformal prediction seems like yet another contender. But conformal prediction can work in combination with any other uncertainty approach and has many advantages that make it stand out:

• Guaranteed coverage: Prediction regions generated by conformal prediction come with coverage guarantees of the true outcome
• Easy to use: Conformal prediction approaches can be implemented from scratch with just a few lines of code
• Model-agnostic: Conformal prediction works with any machine learning model
• Distribution-free: Conformal prediction makes no distributional assumptions
• No retraining required: Conformal prediction can be used without retraining the model
• Broad application: conformal prediction works for classification, regression, time series forecasting, and many other tasks

Sound good?

Then this is the right book for you to learn about this versatile, easy-to-use yet powerful tool for taming the uncertainty of your models.

Introduction To Conformal Prediction With Python 1st Table of contents:

Chapter1 : Introduction

Chapter 2: Content

Chapter 3: Conclusion 

Chapter 4: Appendices

Chapter 5: Glossary

Chapter 6: References 

Chapter 7: Index

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Tags: Christoph Molnar, Conformal Prediction