This is the code repository for Practical Guide to Applied Conformal Prediction, published by Packt.
Learn and apply the best uncertainty frameworks to your industry applications
Embark on an insightful journey with 'Practical Guide to Applied Conformal Prediction in Python', a comprehensive resource that equips you with the latest techniques to quantify uncertainty in machine learning and computer vision models effectively.
This book covers the following exciting features:
- 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 & 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
y_pred_calib = model.predict(X_calib)
y_pred_score_calib = model.predict_proba(X_calib)
y_pred_test = model.predict(X_test)
y_pred_score_test = model.predict_proba(X_test)
Following is what you need for this book: This book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
You will need a working Python environment on your computer. We recommend using Python 3.6 or later. Ensure that you have essential libraries, such as scikit-learn, NumPy, and Matplotlib, installed. If not, you can easily install them using Conda or pip.
The notebooks can be run both locally or by using Google Colab (https://colab.research.google.com).
System requirements are mentioned in the following table:
| Software/Hardware | Operating System requirements |
|---|---|
| Python | Windows, macOS, or Linux |
| Colab (to run notebooks in Google Cloud) | Windows, macOS, or Linux |
| MAPIE | Windows, macOS, or Linux |
| Amazon Fortuna | Windows, macOS, or Linux |
| NIxtla statsforecast | Windows, macOS, or Linux |
| NeuralProphet | Windows, macOS, or Linux |
Valery Manokhin, is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valery has published extensively in leading machine learning journals, and his Ph.D. dissertation ‘Machine Learning for Probabilistic Prediction’ is read by thousands of people across the world. He is also the creator of “Awesome Conformal Prediction,” the most popular resource and GitHub repository for all things Conformal Prediction.
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This is the code repository for "Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications" in Python, published by Packt.
Unlock the secrets of the best 🌟 🌟 🌟 🌟 🌟 and the most rapidly growing 🚀🚀🚀🚀🚀 uncertainty quantification framework of the XXIst century.
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 uncertainty in industry applications. 🌟 🌟 The book will explain how Conformal Prediction differs from traditional machine learning. 🌟
Book Description
🌟 🌟 🌟 Embark on an insightful journey with'Practical Guide to Applied Conformal Prediction in Python', a comprehensive resource that effectively equips you with the latest techniques to quantify uncertainty in machine learning and computer vision models.** 🌟 🌟 🌟
This book covers a wide array of real-world applications, including Conformal Prediction for classification, regression forecasting, computer vision, and NLP, as well as advanced examples for handling imbalanced data and multi-class classification problems. These practical case studies will enable you to apply your newfound knowledge to various industry scenarios.
Designed for data scientists, analysts, machine learning engineers, and industry professionals, this book caters to different skill levels - making it an ideal resource for both beginners and experienced practitioners. Deep dive into the latest Conformal Prediction techniques and elevate your machine learning expertise.
If you're eager to manage uncertainty in industry applications using Python, 'Practical Guide to Applied Conformal Prediction in Python' is your ultimate guide. Order your copy today and propel your career to new heights!
Table of Contents 🌟 Introducing Conformal Prediction 🌟 Overview of Conformal Prediction 🌟 Fundamentals of Conformal Prediction 🌟 Validity and efficiency of conformal prediction 🌟 Types of conformal predictors 🌟 Conformal Prediction for classification 🌟 Conformal Prediction for regression 🌟 Conformal Prediction for time series and forecasting 🌟 Conformal Prediction for Computer Vision 🌟 Conformal Prediction for Natural Language Processing 🌟 Handling imbalanced data 🌟 Multi-class Conformal Prediction