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Practical guide to applied conformal prediction in Python learn and apply the best uncertainty frameworks to your industry applications

"Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications. Explore cutting-edge methods to measure and manage uncertai...

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Bibliographic Details
Main Author: Manokhin, Valery
Other Authors: Sudjianto, Agus (writer of foreword)
Format: eBook
Language:English
Published: Birmingham, UK Packt Publishing Ltd. 2023
Edition:1st edition
Subjects:
Machine Learning / Http://id.loc.gov/authorities/subjects/sh85079324
Python (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh96008834
Apprentissage Automatique
Python (langage De Programmation)
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • Chapter 5: Types of Conformal Predictors
  • Understanding classical predictors
  • Applying TCP for classification problems
  • Applying TCP for regression problems
  • Advantages
  • Understanding inductive conformal predictors
  • Choosing the right conformal predictor
  • Transductive conformal predictors
  • Inductive conformal predictors
  • Summary
  • Part 3: Applications of Conformal Prediction
  • Chapter 6: Conformal Prediction for Classification
  • Classifier calibration
  • Understanding the concepts of classifier calibration
  • Evaluating calibration performance
  • Various approaches to classifier calibration
  • Histogram binning
  • Platt scaling
  • Isotonic regression
  • Conformal prediction for classifier calibration
  • Venn-ABERS conformal prediction
  • Comparing calibration methods
  • Open source tools for conformal prediction in classification problems
  • Nonconformist
  • Summary
  • Chapter 7: Conformal Prediction for Regression
  • Uncertainty quantification for regression problems
  • Understanding the types and sources of uncertainty in regression modeling
  • The concept of prediction intervals
  • Why do we need prediction intervals?
  • Intro
  • Title Page
  • Copyright and Credits
  • Foreword
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Introduction
  • Chapter 1: Introducing Conformal Prediction
  • Technical requirements
  • Introduction to conformal prediction
  • Understanding conformity measures
  • The origins of conformal prediction
  • The future of conformal prediction
  • How conformal prediction differs from traditional machine learning
  • The p-value and its role in conformal prediction
  • Summary
  • Chapter 2: Overview of Conformal Prediction
  • Understanding uncertainty quantification
  • How is it different from a confidence interval?
  • Conformal prediction for regression problems
  • Building prediction intervals and predictive distributions using conformal prediction
  • Mechanics of CQR
  • Quantile regression
  • CQR
  • Jackknife+
  • Jackknife regression
  • Jackknife+ regression
  • Conformal predictive distributions
  • Summary
  • Chapter 8: Conformal Prediction for Time Series and Forecasting
  • UQ for time series and forecasting problems
  • The importance of UQ
  • The history of UQ
  • Early statistical methods
  • the roots of UQ in time series
  • Aleatoric uncertainty
  • Epistemic uncertainty
  • Different ways to quantify uncertainty
  • Quantifying uncertainty using conformal prediction
  • Summary
  • Part 2: Conformal Prediction Framework
  • Chapter 3: Fundamentals of Conformal Prediction
  • Fundamentals of conformal prediction
  • Definition and principles
  • Basic components of a conformal predictor
  • Types of nonconformity measures
  • Summary
  • Chapter 4: Validity and Efficiency of Conformal Prediction
  • The validity of probabilistic predictors
  • Classifier calibration
  • The efficiency of probabilistic predictors
  • Summary

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