Practical Guide To Applied Conformal Prediction In Python PDF Download
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Author | : Valery Manokhin |
Publisher | : Packt Publishing Ltd |
Total Pages | : 240 |
Release | : 2023-12-20 |
Genre | : Mathematics |
ISBN | : 1805120913 |
Download Practical Guide to Applied Conformal Prediction in Python Book in PDF, ePub and Kindle
Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting. Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications Explore cutting-edge methods to measure and manage uncertainty in industry applications Understand how Conformal Prediction differs from traditional machine learning Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.
Author | : Christoph Molnar |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : Machine learning |
ISBN | : |
Download Introduction to Conformal Prediction with Python Book in PDF, ePub and Kindle
Author | : Anastasios N. Angelopoulos |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : COMPUTERS |
ISBN | : 9781638281597 |
Download Conformal Prediction Book in PDF, ePub and Kindle
Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as 90%. It is easy-to-understand, easy-to-use, and in general, applies naturally to problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, amongst others.In this hands-on introduction the authors provide the reader with a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques. They lead the reader through practical theory and examples of conformal prediction and describe its extensions to complex machine learning tasks involving structured outputs, distribution shift, time-series, outliers, models that abstain, and more. Throughout, there are many explanatory illustrations, examples, and code samples in Python. With each code sample comes a Jupyter notebook implementing the method on a real-data example.This hands-on tutorial, full of practical and accessible examples, is essential reading for all students, practitioners and researchers working on all types of systems deploying machine learning techniques.
Author | : Anastasios N. Angelopoulos |
Publisher | : |
Total Pages | : 0 |
Release | : 2023-03-27 |
Genre | : |
ISBN | : 9781638281580 |
Download Conformal Prediction Book in PDF, ePub and Kindle
Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as 90%. It is easy-to-understand, easy-to-use, and in general, applies naturally to problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, amongst others. In this hands-on introduction the authors provide the reader with a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques. They lead the reader through practical theory and examples of conformal prediction and describe its extensions to complex machine learning tasks involving structured outputs, distribution shift, time-series, outliers, models that abstain, and more. Throughout, there are many explanatory illustrations, examples, and code samples in Python. With each code sample comes a Jupyter notebook implementing the method on a real-data example. This hands-on tutorial, full of practical and accessible examples, is essential reading for all students, practitioners and researchers working on all types of systems deploying machine learning techniques.
Author | : Vineeth Balasubramanian |
Publisher | : Newnes |
Total Pages | : 323 |
Release | : 2014-04-23 |
Genre | : Computers |
ISBN | : 0124017150 |
Download Conformal Prediction for Reliable Machine Learning Book in PDF, ePub and Kindle
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Author | : Tarek Amr |
Publisher | : |
Total Pages | : 384 |
Release | : 2020-07-24 |
Genre | : Computers |
ISBN | : 9781838826048 |
Download Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits Book in PDF, ePub and Kindle
Author | : William Gray |
Publisher | : Independently Published |
Total Pages | : 240 |
Release | : 2019-05-31 |
Genre | : |
ISBN | : 9781070792316 |
Download Python Machine Learning Book in PDF, ePub and Kindle
What do you know about Python, are you afraid it's not enough ? Moving to a higher level is your next goal ? ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine learning is changing every aspect of our lives !!! Today, ML algorithms accomplish tasks that until recently only expert humans could perform and, as machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. Programmers who know close to nothing about this technology, now, can use simple, efficient tools to implement programs capable of learning from data. In this practical guide, you will discover: Understand the key frameworks in ML Latest Python open source libraries in ML ML techniques using real-world data The ML Classifiers Using Scikit-Learn Implementing a Multilayer Artificial Neural Network from Scratch The Mechanics of TensorFlow ML Model into a Web Application The future of ML You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Using the latest Python open source libraries, this book offers the practical knowledge you need to create and contribute to machine learning and modern data analysis. Get a copy, of Python Machine Learning, today and see where the future lies ! By scrolling to the top of the page, select " Add to Cart " button ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★
Author | : Rebecca Bilbro |
Publisher | : |
Total Pages | : 350 |
Release | : 2018 |
Genre | : Python (Computer program language) |
ISBN | : 9781491963036 |
Download Applied Text Analysis with Python Book in PDF, ePub and Kindle
With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You’ll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science. This book presents a concise, focused, and applied approach to text analysis with Python, and covers topics including text ingestion and wrangling, basic machine learning on text, classification for text analysis, entity resolution, and text visualization. Applied Text Analysis with Python will enable you to design and develop language-aware data products. You’ll learn how and why machine learning algorithms make decisions about language to analyze text; how to ingest, wrangle, and preprocess language data; and how the three primary text analysis libraries in Python work in concert. Ultimately, this book will enable you to design and develop language-aware data products.
Author | : Stephen J. Taylor |
Publisher | : Princeton University Press |
Total Pages | : 544 |
Release | : 2011-02-11 |
Genre | : Business & Economics |
ISBN | : 1400839254 |
Download Asset Price Dynamics, Volatility, and Prediction Book in PDF, ePub and Kindle
This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.
Author | : Alexander Gammerman |
Publisher | : Springer |
Total Pages | : 235 |
Release | : 2016-04-16 |
Genre | : Computers |
ISBN | : 331933395X |
Download Conformal and Probabilistic Prediction with Applications Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, held in Madrid, Spain, in April 2016. The 14 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 23 submissions and cover topics on theory of conformal prediction; applications of conformal prediction; and machine learning.