دانلود کتاب 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

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.

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?