The economist Lloyd Shapley developed the concept of Shapley values, which is an attribution system based on the theory of cooperative games. It is a useful term for describing the results produced by a machine learning model. The Shapley value is a concept that is used in game theory to determine the contribution that each player makes in a game that is either cooperative or played in a coalition. In recent times, it has garnered interest as an efficient method to explain the predictions of machine learning models.
The Shapley value is a well-known strategy that was developed from the concept of cooperative game theory and has useful properties. For instance, it is frequently used in the lending industry to explain why a particular individual was not approved for a loan using machine learning algorithms. The reader of this post will be provided with the most useful resources for furthering their understanding of Shapley Values in Machine Learning.
Coursera’s Machine Learning Modeling Pipelines in Production is available right now.
The following topics will be covered in this course as part of the Machine Learning Engineering for Production Specialization:
You will design models for a variety of service contexts, use tools and techniques to successfully manage your modelling resources, and use analytics tools and performance indicators to address issues of model fairness and explainability as well as bottlenecks.
The purpose of machine learning engineering for production is to assist you in developing skills that are prepared for production by combining the fundamental concepts of machine learning with the functional understanding of modern software development and engineering roles.
By Christoph Molnar: Interpretable Machine Learning
This book explores the topic of making machine learning models and the judgments they produce more interpretable. In this lesson, you will learn about generic model-independent methods for analysing black box models, such as feature importance and accumulated local effects. Additionally, you will learn how to explain individual predictions by utilising Shapley values and LIME. Each method of interpretation is thoroughly discussed and subjected to in-depth discussion. You will learn how to select and properly execute the most effective interpretation method for your machine-learning project after going through this guide.
An Introduction to Machine Learning, Covering Everything from Theory to Algorithms
The objective of this textbook is to provide a methodical presentation of machine learning and the computational concepts underlying it. This book provides a complete theoretical examination of the fundamental concepts that underpin machine learning, as well as the mathematical derivations that transform these principles into practical algorithms. The book may be purchased here. After providing an overview of the principles of the area, the book continues on to discuss numerous important topics that were not included in prior textbooks.
A review of the computational difficulty of learning, major algorithmic paradigms, and developing theoretical notions such as the PAC-Bayes approach and compression-based bounds are included in this article. Students are able to understand the fundamentals as well as the strategies that are involved in machine learning thanks to the text. It is intended for use in a beginning graduate-level course or an advanced undergraduate course.
MLIS 2020 is an acronym that stands for Machine Learning and Artificial Intelligence.
The Machine Learning and Intelligent Systems Conference (MLIS 2020) was the most current in a series of annual conferences that have been created to encourage the exchange of information regarding the most recent scientific and technological breakthroughs in machine learning and intelligent systems. In addition, the yearly conference fosters relationships among members of the scientific community working in interrelated fields of study. The publication contains 53 essays that were selected from a total of more than 160 submissions and presented at MLIS 2020. Some of the areas that are covered include data mining, image processing, neural networks, human health, natural language processing, video processing, computational intelligence, expert systems, human-computer interaction, deep learning, and robotics. Because it presents an overview of current research and achievements in machine learning and artificial intelligence, this book will be of interest to anyone working on the subject because it contains such information.
