Practical Guide to Applied Conformal Prediction in Python : Learn and Apply the Best Uncertainty Frameworks to Your Industry Applications

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...

Descripción completa

Detalles Bibliográficos
Otros Autores: Manokhin, Valery, author (author), Sudjianto, Agus, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing [2023]
Edición:First edition
Materias:
Python (Computer program language)
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009790335106719
Tabla de Contenidos:
  • 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
  • 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
  • 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?
  • 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
  • Modern machine learning approaches
  • The concept of PIs in forecasting applications
  • Definition and construction
  • The importance of forecasting applications
  • Challenges and considerations
  • Various approaches to producing PIs
  • Parametric approaches
  • Non-parametric approaches
  • Bayesian approaches
  • Machine learning approaches
  • Conformal prediction
  • Conformal prediction for time series and forecasting
  • Ensemble batch PIs (EnbPIs)
  • NeuralProphet
  • Summary
  • Chapter 9: Conformal Prediction for Computer Vision
  • Uncertainty quantification for computer vision
  • Why does uncertainty matter?
  • Types of uncertainty in computer vision
  • Quantifying uncertainty
  • Why does deep learning produce miscalibrated predictions?
  • Post-2012 - the deep learning surge.
  • The "calibration crisis" in deep learning - a turning point in 2017
  • Overconfidence in modern deep learning computer vision models
  • Various approaches to quantify uncertainty in computer vision problems
  • The superiority of conformal prediction in uncertainty quantification
  • Conformal prediction for computer vision
  • Uncertainty sets for image classifiers using conformal prediction
  • Building computer vision classifiers using conformal prediction
  • Naïve Conformal prediction
  • Adaptive Prediction Sets (APS)
  • Regularized Adaptive Prediction Sets (RAPS)
  • Summary
  • Chapter 10: Conformal Prediction for Natural Language Processing
  • Uncertainty quantification for NLP
  • What is uncertainty in NLP?
  • Benefits of quantifying uncertainty in NLP
  • The challenges of uncertainty in NLP
  • Understanding why deep learning produces miscalibrated predictions
  • Introduction to deep learning in NLP
  • Challenges with deep learning predictions in NLP
  • The implications of miscalibration
  • Various approaches to quantify uncertainty in NLP problems
  • Bootstrap methods and ensemble techniques
  • Out-of-distribution (OOD) detection
  • Conformal prediction for NLP
  • How conformal prediction works in NLP
  • Practical applications of conformal prediction in NLP
  • Advantages of using conformal prediction in NLP
  • Summary
  • Part 4: Advanced Topics
  • Chapter 11: Handling Imbalanced Data
  • Introducing imbalanced data
  • Why imbalanced data problems are complex to solve
  • Methods for solving imbalanced data
  • The methods for solving imbalanced data
  • Solving imbalanced data problems by applying conformal prediction
  • Addressing imbalanced data with Venn-Abers predictors
  • Key insights from the Credit Card Fraud Detection notebook
  • Summary
  • Chapter 12: Multi-Class Conformal Prediction
  • Multi-class classification problems.
  • Algorithms for multi-class classification
  • One-vs-all and one-vs-one strategies
  • Metrics for multi-class classification problems
  • Confusion matrix
  • Precision
  • Recall
  • F1 score
  • Macro- and micro-averaged metrics
  • Area Under Curve (AUC-ROC)
  • Log loss and its application in measuring calibration of multi-class models
  • Brier score and its application in measuring the calibration of multi-class models
  • How conformal prediction can be applied to multi-class classification problems
  • Multi-class probabilistic classification using inductive and cross-Venn-ABERS predictors
  • Summary
  • Index
  • Other Books You May Enjoy
  • Blank Page.

Ejemplares similares