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9.5 Shapley Values. A prediction can be explained by assuming that each feature value of the instance is a “player” in a game where the prediction is the payout. Shapley values – a method from coalitional game theory – tells us how to fairly distribute the “payout” among the features.
- 2.2 What Is Machine Learning
Step 2: Enter this information into a machine learning...
- 11.2 The Future of Interpretability
Machine learning will be automated and, with it,...
- 3.6 Human-friendly Explanations
What it means for interpretable machine learning: Good...
- 5.3 Glm, GAM and More
5.3.1 Non-Gaussian Outcomes - GLMs. The linear regression...
- PDP
8.1 Partial Dependence Plot (PDP). The partial dependence...
- 12 Contribute to the Book
Machine learning algorithms usually operate as black boxes...
- 2.2 What Is Machine Learning
- What Questions Do Shapley Values answer?
- What Are Shapley Values?
- What’s So Great About The Shapley Value?
- How Are Shapley Values Used in The Context of Ml Models?
- Limitations of Shapley Values
- Shapley Values: A Powerful Explanation Tool
At a high level, the Shapley value approach attempts to explain why an ML model reports the outputs that it does on an input. For example, for a model discerning whether an applicant should receive a mortgage, we might want to know why a retiree Jane specifically has been denied a loan, or why the model believes her chance of defaulting is 70% — th...
Shapley values are a concept borrowed from the cooperative game theory literature and date back to the 1950s. In their original form, Shapley values were used to fairly attribute a player’s contribution to the end result of a game. Suppose we have a cooperative game where a set of players each collaborate to create some value. If we can measure the...
The Shapley value is special in that it is the only value that satisfies a set of powerful axioms that you might desire in an attribution strategy. We won’t cover the proof of this uniquenessin this post, but let’s highlight a few of these axioms: 1. Completeness/Efficiency: the sum of the contribution of each player (feature) must add up to the to...
We mentioned that Shapley values calculate the average marginal contributionof a feature 𝒙 towards a model score. How is this computed in practice? Let’s first try to formulate this for a cooperative game. Suppose that we have an n-player game with players 1, 2, …, n and some value function v that takes in a subset of the players and returns the r...
Despite their axiomatic motivation, there are some notable limitations of Shapley values and their use in ML settings. Computing Shapley values requires selection of coalitions or subsets of features, which scales exponentially in the number of features. This is abated by using approximation techniques and taking advantage of specific model structu...
Shapley values borrow insights from cooperative game theory and provide an axiomatic way of approaching machine learning explanations. It is one of the few explanation techniques that are backed in intuitive notions of what a good explanation looks like, it allows for both local and global reasoning, and it is agnostic to model type. As a result, m...
- Divya Gopinath
Shapley values are a widely used approach from cooperative game theory that come with desirable properties. This tutorial is designed to help build a solid understanding of how to compute and interpet Shapley-based explanations of machine learning models.
SHAP (SHapley Additive exPlanations) values are a way to explain the output of any machine learning model. It uses a game theoretic approach that measures each player's contribution to the final outcome. In machine learning, each feature is assigned an importance value representing its contribution to the model's output.
11 de feb. de 2022 · Abstract: Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game theory and axiomatic properties of the Shapley value.
- arXiv:2202.05594 [cs.LG]
- this https URL
1 de oct. de 2021 · SHAP is an increasingly popular method used for interpretable machine learning. This article breaks down the theory of Shapley Additive Values and illustrates with a few practical examples. Khalil Zlaoui. ·. Follow. Published in. Towards Data Science. ·. 8 min read. ·. Oct 1, 2021. Photo by Johannes Plenio on Unsplash. Introduction.
In machine learning The Shapley value provides a principled way to explain the predictions of nonlinear models common in the field of machine learning . By interpreting a model trained on a set of features as a value function on a coalition of players, Shapley values provide a natural way to compute which features contribute to a ...
