Shapley Additive Explanations (SHAP) is a powerful method for interpreting and explaining machine learning model predictions by attributing importance scores to input features.
Machine learning models have become increasingly complex, making it difficult for users to understand and trust their predictions. SHAP addresses this issue by providing a way to explain the contributions of each feature to a model's prediction for a specific instance. This method is based on the concept of Shapley values, which originate from cooperative game theory and offer a fair way to distribute rewards among players.
Recent research has focused on improving the efficiency and applicability of SHAP in various contexts. For example, ensemble-based modifications have been proposed to simplify SHAP for cases with a large number of features. Other studies have explored the use of imprecise SHAP for situations where class probability distributions are uncertain. Researchers have also investigated the relationship between SHAP explanations and the underlying physics of power systems, demonstrating that SHAP values can capture important physical properties.
In addition to these advancements, researchers have proposed Counterfactual SHAP, which incorporates counterfactual information to produce more actionable explanations. This approach has been shown to be superior to existing methods in certain contexts. Furthermore, the stability of SHAP explanations has been studied, revealing that the choice of background data size can impact the reliability of the explanations.
Practical applications of SHAP include its use in healthcare, where it has been employed to interpret gradient-boosting decision tree models for hospital data, and in cancer research, where it has been used to analyze the risk factors for colon cancer. One company case study involves the use of SHAP in the financial sector, where it has been applied to credit scoring models to provide insights into the factors influencing credit risk.
In conclusion, SHAP is a valuable tool for interpreting complex machine learning models, offering insights into the importance of input features and enabling users to better understand and trust model predictions. As research continues to advance, SHAP is expected to become even more effective and widely applicable across various domains.
Shapley Additive Explanations (SHAP)
Shapley Additive Explanations (SHAP) Further Reading
1.Ensembles of Random SHAPs http://arxiv.org/abs/2103.03302v1 Lev V. Utkin, Andrei V. Konstantinov2.An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data http://arxiv.org/abs/2106.09111v1 Lev V. Utkin, Andrei V. Konstantinov, Kirill A. Vishniakov3.Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations http://arxiv.org/abs/2209.05793v1 Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos4.Counterfactual Shapley Additive Explanations http://arxiv.org/abs/2110.14270v4 Emanuele Albini, Jason Long, Danial Dervovic, Daniele Magazzeni5.An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models http://arxiv.org/abs/2204.11351v3 Han Yuan, Mingxuan Liu, Lican Kang, Chenkui Miao, Ying Wu6.SHAP for additively modeled features in a boosted trees model http://arxiv.org/abs/2207.14490v1 Michael Mayer7.The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits http://arxiv.org/abs/2007.14045v3 Marcelo Arenas, Pablo Barceló Leopoldo Bertossi, Mikaël Monet8.Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital http://arxiv.org/abs/2112.11071v2 Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima, Naoki Nakashima9.Explanation of Machine Learning Models of Colon Cancer Using SHAP Considering Interaction Effects http://arxiv.org/abs/2208.03112v1 Yasunobu Nohara, Toyoshi Inoguchi, Chinatsu Nojiri, Naoki Nakashima10.Shapley values for feature selection: The good, the bad, and the axioms http://arxiv.org/abs/2102.10936v1 Daniel Fryer, Inga Strümke, Hien NguyenShapley Additive Explanations (SHAP) Frequently Asked Questions
What is the Shapley Additive Explanations (SHAP) approach?
Shapley Additive Explanations (SHAP) is a method for interpreting and explaining machine learning model predictions by attributing importance scores to input features. It helps users understand and trust complex models by providing insights into the contributions of each feature to a model's prediction for a specific instance. SHAP is based on the concept of Shapley values, which originate from cooperative game theory and offer a fair way to distribute rewards among players.
What is the difference between SHAP and Shapley values?
Shapley values are a concept from cooperative game theory that provides a fair way to distribute rewards among players in a game. SHAP (Shapley Additive Explanations) is a method that applies Shapley values to machine learning models, attributing importance scores to input features and explaining the contributions of each feature to a model's prediction for a specific instance. While Shapley values are a more general concept, SHAP specifically focuses on interpreting and explaining machine learning models.
How do you explain Shapley values?
Shapley values are a concept from cooperative game theory that provides a fair way to distribute rewards among players in a game. They are calculated by considering all possible permutations of players and determining the marginal contribution of each player to the total reward. The Shapley value for a player is the average of their marginal contributions across all permutations. This ensures that each player's contribution is fairly recognized, taking into account the interactions between players and their individual impact on the game's outcome.
What is Shapley Additive Explanations medium?
Shapley Additive Explanations (SHAP) medium refers to the various ways in which SHAP values can be visualized and communicated. These mediums include plots, graphs, and other visual representations that help users understand the importance of input features and their contributions to a model's prediction for a specific instance. By using these mediums, users can gain insights into the inner workings of complex machine learning models and better trust their predictions.
What is the explanation of SHAP plots?
SHAP plots are visual representations of the Shapley Additive Explanations (SHAP) values for a machine learning model. They help users understand the importance of input features and their contributions to a model's prediction for a specific instance. A SHAP plot typically displays the features on the x-axis and their corresponding SHAP values on the y-axis. Each point on the plot represents the SHAP value for a specific feature in a particular instance. By analyzing these plots, users can gain insights into the inner workings of complex machine learning models and better trust their predictions.
How is SHAP used in practical applications?
SHAP has been used in various practical applications, including healthcare, cancer research, and the financial sector. In healthcare, it has been employed to interpret gradient-boosting decision tree models for hospital data. In cancer research, it has been used to analyze the risk factors for colon cancer. In the financial sector, one company case study involves the use of SHAP in credit scoring models to provide insights into the factors influencing credit risk. These applications demonstrate the versatility and usefulness of SHAP in interpreting complex machine learning models across different domains.
What are the recent advancements in SHAP research?
Recent research in SHAP has focused on improving its efficiency and applicability in various contexts. Some advancements include ensemble-based modifications for cases with a large number of features, the use of imprecise SHAP for situations with uncertain class probability distributions, and the investigation of the relationship between SHAP explanations and the underlying physics of power systems. Researchers have also proposed Counterfactual SHAP, which incorporates counterfactual information to produce more actionable explanations, and studied the stability of SHAP explanations, revealing the impact of background data size on the reliability of the explanations.
How can SHAP help non-experts understand machine learning models?
SHAP provides a way for non-experts to understand complex machine learning models by attributing importance scores to input features and explaining the contributions of each feature to a model's prediction for a specific instance. By visualizing these explanations through SHAP plots and other mediums, users can gain insights into the inner workings of the models and better trust their predictions. This increased understanding and trust can help non-experts make more informed decisions based on the outputs of machine learning models.
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