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Introduction To Conformal Prediction With Python: A Short Guide For Quantifying Uncertainty Of Machine Learning Models
Introduction To Conformal Prediction With Python is the quickest way to learn an easy-to-use and very general technique for uncertainty quantification.
"This concise book is accessible, lucid, and full of helpful code snippets. It explains the mathematical ideas with clarity and provides the reader with practical examples that illustrate the essence of conformal prediction, a powerful idea for uncertainty quantification."
– Junaid Butt, Research Software Engineer, IBM Research"Modern statistics can be a difficult topic, but Christoph has managed to make it feel easy, practical, and fun! Reading this book is a great first step towards gaining mastery of conformal prediction and related topics."
– Anastasios Angelopoulos, Researcher at the University of California, Berkeley
SummaryA 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
Guaranteed Prediction regions generated by conformal prediction come with coverage guarantees of the true outcomeEasy to Conformal prediction approaches can be implemented from scratch with just a few lines of Conformal prediction works with any machine learning Conformal prediction makes no distributional assumptionsNo retraining Conformal prediction can be used without retraining the modelBroad conformal prediction works for classification, regression, time series forecasting, and many other tasksSound 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.
This
Teaches the intuition behind conformal predictionDemonstrates how conformal prediction works for classification and regressionShows how to apply conformal prediction using Python and MAPIEEnables you to quickly learn new conformal algorithmsWith the knowledge in this book, you'll be ready to quantify the uncertainty of any model.
"This book is a comprehensive guide and resource for anyone who wants to learn how to quantify uncertainty with conformal prediction by using python. Christoph's writing is clear and engaging. He provides practical examples that help readers understand how to apply conformal prediction techniques/concepts to real-world problems."
– Tony Zhang, Data Scientist at Munich Re
"This concise book is accessible, lucid, and full of helpful code snippets. It explains the mathematical ideas with clarity and provides the reader with practical examples that illustrate the essence of conformal prediction, a powerful idea for uncertainty quantification."
– Junaid Butt, Research Software Engineer, IBM Research"Modern statistics can be a difficult topic, but Christoph has managed to make it feel easy, practical, and fun! Reading this book is a great first step towards gaining mastery of conformal prediction and related topics."
– Anastasios Angelopoulos, Researcher at the University of California, Berkeley
SummaryA 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
Guaranteed Prediction regions generated by conformal prediction come with coverage guarantees of the true outcomeEasy to Conformal prediction approaches can be implemented from scratch with just a few lines of Conformal prediction works with any machine learning Conformal prediction makes no distributional assumptionsNo retraining Conformal prediction can be used without retraining the modelBroad conformal prediction works for classification, regression, time series forecasting, and many other tasksSound 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.
This
Teaches the intuition behind conformal predictionDemonstrates how conformal prediction works for classification and regressionShows how to apply conformal prediction using Python and MAPIEEnables you to quickly learn new conformal algorithmsWith the knowledge in this book, you'll be ready to quantify the uncertainty of any model.
"This book is a comprehensive guide and resource for anyone who wants to learn how to quantify uncertainty with conformal prediction by using python. Christoph's writing is clear and engaging. He provides practical examples that help readers understand how to apply conformal prediction techniques/concepts to real-world problems."
– Tony Zhang, Data Scientist at Munich Re
124 pages, Kindle Edition
Published February 20, 2023
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