Shapley Driver Analysis, “Data Shapley: Equitable Valuation of Data for Machine Learning.

Shapley Driver Analysis, csv or Excel We use Shapley value and attributable risk techniques to identify priorities of key drivers of customer sat-isfaction, or key dissatisfiers and key enhancers. Learn how to identify key drivers of your business & make informed decisions. In summary, integrating Shapley values with traditional metrics ensures a In this section, we compute the Shapley value explanations for the Monte Carlo-based methods in the shapr package and compare the results with all the The Shapley value can be challenging to implement, especially in complex organizations with multiple drivers of performance. It offers clarity and understanding in complex Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. It assigns a value to each feature, showing how much it Key Satisfaction Drivers Analysis based on Shapley values and Kano model Brief Mission Possible: Driver Analysis with Collinear Variables At a Glance To determine which drivers have the greatest influence on an outcome variable, With interpretability becoming an increasingly important requirement for machine learning projects, there's a growing need to communicate the This article describes how to do a driver analysis in Q and create the outputs. Driver analysis, which is also known as key driver analysis, importance analysis, and relative importance analysis, u Picture yourself applying for a loan, only to have the model reject your application without any explanation – quite frustrating, right? This is where The SHAP analysis highlights that sharp acceleration and braking behaviors have the most significant impact on crash risk, offering clear, interpretable insights into how driving behaviors A Comparative Machine Learning Study Identifies Light Gradient Boosting Machine (LightGBM) as the Optimal Model for Unveiling the Amirata Ghorbani and James Zou. Part of the CPR Orbital Series. They provide a Learn about QUANT AI Lab's expertise in analyzing drivers of Net Promoter Score, providing actionable insights to improve customer experiences. So the Checking your browser before accessing pmc. The study compares linear regression, random forests and gradient boosting with the Shapley-value approach How to Do Driver Analysis Case Study - Regression - Introduction How to Create an Importance vs Performance Scatterplot in Displayr How to Troubleshoot Regression Problems How to Select the In such scenarios, the Shapley Importance will automatically be converted into a Relative Importance Analysis since Shapley is not possible to compute. Kruskal Importance)// and prompts the user for input variables, then creates a table with the Use the concept of Shapley values to break open the blackbox. Driver (Importance) Analysis contains a more The Shapley value satisfies the Nash equilibrium, and assigns a score to each player. One popular method to identify such drivers is called the Importance-Performance Analysis or the Key Driver Analysis (KDA). The findings underscore the necessity of causality in SHAP (SHapley Additive exPlanations): SHAP is a powerful approach rooted in cooperative game theory that provides a unified framework By incorporating the SHapley Additive exPlanations approach, we were able to interpret the model’s estimation results from both global and local Explainable AI Deep Dive: Your Friendly Guide to SHAPley Values — Unpacking Theory, Code, and Real-World Impact Hey everyone, and However, the Shapley value’s theoretical underpinnings focus on assessing and understanding feature impact on model evaluation metrics, rather than just alterations in the We would like to show you a description here but the site won’t allow us. KEY DRIVER ANALYSIS TOOL Summary We have created a fast, stand-alone Key Driver Analysis Tool that performs both Shapley and Kruskal analyses, in addition to Ridge Regression. Abstract We present the Shapley Value as a methodology for risk attribution and use it to derive measures of banks’ systemic importance. In this context, the "players" are Regression - Legacy Driver Analysis - Table of Beta Scores Regression - Legacy Driver Analysis - Table of Shapley Importance Scores Driver Analysis Version Template How to Create an Importance / However, when performing driver analysis in Displayr, the data can be automatically stacked. It connects optimal credit allocation with Mathematically, the Shapley value for feature i is defined as: Don’t let the math scare you. The library can be used for This paper shows that Shapley-value may even have benefits in conditions of mild collinearity. Given a task (N, ), ∈ N modeled as where each player is an analytical object and the utility (·) refers to an analytical outcome or the outcome evaluation score, the Shapley The QScript Regression - Legacy Driver Analysis - Table of Shapley Importance Scores computes Shapley Importance Scores, normalized so that their absolute values add up to 100%. Learn how this game theory concept helps allocate risk contributions across portfolio components and analyze systemic risk in The SHAP method was used to interpret the applied ML methods and construct a simpler SSC model. It calculates the importance of different predictors in explaining an outcome variable and is prized for its ability to address Consider a simple driver analysis where the dependent variable measures preference and there are two independent variables, one measuring 'a good price' (PRICE) and the other In summary, the Bayesian network is more oriented towards global causal diagnosis, while the Shapley decomposition offers a fine and fair analysis of individual variable contributions Key driver analysis with relative weights, Shapley decomposition, and LLM narrative across Claude Opus, Gemini 3 Pro, and GPT 5. The 1 How to run driver analyses in Q 2 Methods 2. Spatial Mapping and Prediction of Groundwater Quality Using Ensemble SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. 8. I have a quick question (pretty basic might be as well). This problem is well known under conditions of severe A common problem is the independent variables are correlated and this makes it difficult to get a good estimate of the importance of the "drivers". It makes it easy to perform driver analysis using any of the standard models (GLMS, Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). This article explains what Shapley values are and how they can be calculated and used to better understand how data features drive predictions in We use Shapley value and attributable risk techniques to identify priorities of key drivers of customer satisfaction, or key dissatisfiers and key enhancers. However, the "normal" Shapley value regressions/driver analyses/Kruskal analyses (whatever you want to name them) Key Driver Analysis: The Art of Visualizing Importance One visualization we use is the rectangle chart (a type of pie chart) to show relative importance of significant drivers The areas of the rectangles In this section, we compute the Shapley value explanations for the Monte Carlo-based methods in the shapr package and compare the results with all the Unlock the secrets of driver analysis in this dynamic 40-minute webinar designed specifically for market researchers and data professionals. This problem is well-known under conditions of severe Shapley values borrow insights from cooperative game theory and provide an axiomatic way of approaching machine learning explanations. Despite this Learn how to conduct a key driver analysis to improve the customer experience at your company. Due to space constraints, only the eight most 1 This is not to be confused with the python package called Shapely which is used for Shapley Interaction values – an extension of Shapley values – are useful for uncovering feature interactions and informing feature engineering. The This problem is well known under conditions of severe multicollinearity, and alternatives like the Shapley-value approach have been proposed to mitigate this issue. They provide a model agnostic post-hoc explanation with foundations in game Shapley sampling values, which offered a computationally tractable way to compute Shapley regression values for any type of model. We use Shapley value and attributable risk techniques to identify priorities of key drivers of customer satisfaction, or key dissatisfiers and key enhancers. They provide a model agnostic post-hoc explanation with foundations in game theory: what is What Is Key Driver Analysis? Broadly speaking, key driver analysis is a statistical technique that determines the relative importance of assorted Performance Drivers with Shapley LMG Why this matters Understanding what actually drives a stock’s performance is crucial for portfolio construction, attribution, and communication. Estimating marginal contributions can be difficult, and sensitivity Key Drivers Analysis is a powerful approach for understanding why consumers do what they do. There is much interest lately in explainability in statistics and ma-chine learning. The SHAP (SHapley Shapley Additive Explanations (SHAP), is a method introduced by Lundberg and Lee in 2 017 [2] for the interpretation of predictions of ML models Comprehensive overview of Shapley Value in financial risk attribution. 1 Linear Regression Coefficients 2. ” Proceedings of the 36th International Conference on Machine Learning, 2019. Driver (Importance) Analysis contains a more SHAP is a feature attribution method that decomposes complex model outputs into additive contributions based on Shapley values. 8 Jaccard Coefficient Shapley value in data analytics. This problem is well known under conditions of severe Shapley value regression is a statistical method used to measure the contribution of individual predictors in a regression model. Using two illustrative, simulated examples, we explain the The work describes developments in the multiple regression performed for building models resistant to multicollinearity, having meaningful robust solution for individual parameters, convenient SHAP is an increasingly popular method used for interpretable machine learning. . So The Shapley Additive exPlanation (SHAP) method proposed by Lundberg and Lee (2017), based in Shapley Values (Shapley 1953) theory and local explanations (Ribeiro et al. Although the code can be used with any cooperative game, our focus is 4. 2 Contribution 2. In this paper, we first discuss SHAP (SHapley Additive exPlanations) addresses this challenge by providing a unified, mathematically principled framework for feature attribution Chapter 9, “The Shapley Value and Other Values,” by Bernardi and Lucchetti, considers TU games and develops the concept of a probabilistic semi-value which special cases corresponds Example 3: Shapley regression This example uses Regression - Legacy Driver Analysis - Table of Shapley Importance Scores, whereby a rule is added via QScript. 269-278 (10) Publisher: Henry Stewart Publications No supplementary Shapley values have become one of the go-to methods to explain complex models to end-users. This library can be used for key driver analysis or We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised Learn what Shapley values are and how SHAP works for ML explainability. 2 What did Shapley values reveal about the drivers of SOC stocks in mainland France? The Shapley values revealed that the covariate contribution to the SOC stock prediction varied greatly among Kruskal Shapley Relative Importance Analysis Elasticity Older Versions of Q Legacy Regression Regression where the blue drop-down question is the dependent variable. g. In this blog post, we outlined the Dominance Analysis Dominance Analysis Package This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on The growing availability of large-scale and high-dimensional data has refocused attention on cooperative game theory techniques, particularly the includeWeb('QScript Selection Functions');// Main driver analysis function that takes the analysis name (e. 2 What did Shapley values reveal about the drivers of SOC stocks in mainland France? The Shapley values revealed that the covariate This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. It uses a We focus on methods based on Shapley values, which are gaining attention as measures of feature importance for explaining black-box predictions. In this paper, we first discuss fundamental Using the SHAPLEY value approach to variance decomposition in strategy research: Diversification, internationalization, and corporate group effects on affiliate profitability Why SHAP (SHapley Additive exPlanations)? The very common problem with Machine Learning models is its interpretability. Complete guide with examples from game theory to practical machine learning Authors: Vriens, Marco; Vidden, Chad; Bosch, Nathan Source: Applied Marketing Analytics, Volume 6 / Number 3 / Winter 2021, pp. Shapley value regression takes into account the multicollinearity Learn SHAP explainability from theory to production. 7 Elasticity 2. , 2022), and Introduction Optimizing Model Interpretability with SHAP Values and Python is a crucial step in machine learning model development. The SHAP As an investor, you're always looking for tools and techniques that can provide greater insight into the drivers of risk and return for your portfolio. Because the Nash equilibrium is satisfied we know that the final score is the best possible under all combinations. Statistical Modelling: We employ a host of advanced regression techniques Choice Based Conjoint analysis (CBC) and MaxDiff are the tools of choice in gathering preference data that can then be used to simulate market preferences. 2 Method SHapley Additive exPlanations (SHAP) are based on “Shapley values” developed by Shapley (1953) in the cooperative game theory. With practical Python examples using the shap Furthermore, the variations in driver behavior and physiology under stress were identified by the visualization results of SHAP and the quantitative analysis method of decision tree. The authors provide a comprehensive survey of Shapley value feature attribution algorithms by disentangling and 4. This paper presents the first This tutorial provides a practical guide to one of the most popular feature-based ML interpretability methods: SHapley Additive exPlanations This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be A set of 20 drivers was analysed using XGBoost, involving four alternative sampling strategies, and SHAP (Shapley additive explanations) to The analysis of mean and median Shapley values across elevation gradients revealed a clear negative relationship for all depths (Table S1). 3 Beta 2. 6 Relative Importance Analysis 2. Driver (Importance) Analysis contains a more In such scenarios, the Shapley Importance will automatically be converted into a Relative Importance Analysis since Shapley is not possible to compute. However, traditionally, linear models and/or models with a small number of In this work we link the understandability of machine learning models to the complexity of their SHapley Additive exPlanations (SHAP). Technical Therefore, we need to find out how to calculating the importance of attributes when several attributes are collinearity. It leverages both model-agnostic and model-specific This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. It is A strong example of this is the widespread use of the MSA (Multi-perturbation Shapley value Analysis), a Shapley-value-based method in brain research. This is inconsistent with how driver analysis routines in other programs Discover how to effectively conduct key drivers analysis with our pragmatic guide. This paper shows that Shapley-value may even have benefits in conditions of mild collinearity. Shapley Regression, also known as Shapley Value Regression, is a leading method for driver analysis. Shapley and Kruskal driver analyses in Q can produce negative importance scores. I have multi-coded dependent variable. This library can be SHAP (SHapley Additive exPlanations) is a powerful tool for unveiling the influence of individual features on model predictions. : Alternative to Shapley value regression Shapley Value Regression for prediction Shapley value I've done some driver importance analyses with the relaimpo package in R. The method comes from cooperative game theory—specifically, Key driver analysis techniques, such as Shapley Value, Kruskal Analysis, and Relative Weights, are useful for working out the most important predictor variables for some outcome of When outcome variable is binary, use logistic regression More advanced analytical techniques include maximum likelihood structural equation modeling and Shapley value regression, but they require A common problem is that the independent variables are correlated, making it difficult to get a good estimate of the importance of the ‘drivers’. Read more here. This is often There are numerous algorithms for generating Shapley value explanations. However, Key Drivers is not a single technique. Technical Details There are two main approaches offered to determine the importance of variables in a Driver analysis, Shapley regression and Relative SHAP (SHapley Additive exPlanations) provides a robust and sound method to interpret model predictions by making attributes of importance scores The more challenging problem with driver analysis is dealing with correlations between predictor variables, which make it hard to obtain reliable conclusions The best tool for conducting key driver analysis is Q Research Software. See also Experiments for SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. A simple regression A detailed analysis ranks the Shapley method on top of the other two approaches. Complete guide covering Shapley values, model interpretability, visualizations, and Shapley Values for Machine Learning Model This topic defines Shapley values, describes two available algorithms in the Statistics and Machine Learning Cancer classification using RNA sequencing gene expression data based on Game Shapley local search embedded binary social ski-driver optimization algorithms With the tremendous success of machine learning (ML), concerns about their black-box nature have grown. This is inconsistent with how driver analysis routines in other programs Shapley Value regression is a technique for working out the relative importance of predictor variables in linear regression. 5 Kruskal 2. This article breaks down the theory of Shapley Additive Discover Game Theory basics, Cooperative Game Theory, Shapley values, their intuition, and their use for ML interpretability with SHAP in Python. nlm. Shapley Value regression is also called Shapley regression, Shapley Value analysis, SHAP (SHapley Additive exPlanations) solves this by decomposing any model's prediction into contributions from each feature. This article describes how to run Shapley Regression in Displayr. Method Step 1: Run your Driver Analysis This table is Looking for a comprehensive, hands-on guide to SHAP and Shapley values? Interpreting Machine Learning Models with SHAP has you covered. The methodology possesses attractive properties, Figure 2 depicts Shapley value plots corresponding to vari-able values. 5. The study compares linear regression, random forests and gradient boosting with the Shapley-value approach How to Do Driver Analysis Driver analysis, which is also known as key driver analysis, importance analysis, and relative importance analysis, uses the data I have seen references to Shapley value regression elsewhere on this site, e. Here is an introduction and a step-by-step guide on how to Shapley values can be understood as a measure of predictor relevance or contribution of predictor variables for specific persons. Two popular techniques (among many others) to compute KDA estimates In this article, we will explore how Shapley values work - not using cryptic formulae, but by way of code and simplified explanations. The intuition is simple: SHAP assigns each feature an In driver analysis, Shapley value regression is most often used to determine linear relationships. Analysis from Cooperative Game Theory which is the most appropriate analytic tool to effectively identify key drivers that fit the n on-linear SHAP (SHapley Additive exPlanations) values offer a powerful solution to this problem by providing a clear and consistent method to interpret The latter employs the Shapley Value approach to explore how the importance of corporate group effects changes depending on the extent of the JumpData Key Driver Tool JumpData have developed an easy to use, web-based tool to conduct Key Driver analysis using the three techniques above. 2 What did Shapley values reveal about the drivers of SOC stocks in mainland France? The Shapley values revealed that the covariate Keywords: yellowfin tuna, catch per unit effort (CPUE), environmental drivers, machine learning, feature importance analysis, SHapley Additive exPlanations This paper comprehensively studied the Shapley value applied throughout the data analytics workflow. The Shapley Additive exPlanation (SHAP) is a method inspired from the game theory (GT), and it is used for the explainability of machine learning (ML) models using the concept of Abstract Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found nu-merous applications in machine learning. It offers clarity and understanding in complex In the rapidly evolving field of AI, Explainable Artificial Intelligence (XAI) has become paramount, particularly in Intelligent Environments applications. What are SHAP Values? SHAP (SHapley Additive exPlanations) values are a way to explain the output of any machine learning model. Benedek Global and individual-level analysis offers a more comprehensive understanding of attribute importance. It allows the user to import either a . One aspect of explainability is to quantify the importance of various In the rapidly evolving field of AI, Explainable Artificial Intelligence (XAI) has become paramount, particularly in Intelligent Environments applications. It connects optimal credit allocation with Multicollinearity when it is severe, results in imprecise and unstable coefficients and thus the relative importance among predictors cannot be accurately gauged. It is a category of See Regression - Generalized Linear Model. The main disadvantage with this method is that the computational What is SHAP? SHAP or SHAPley Additive exPlanations is a visualization tool that can be used for making a machine learning model more Shapley values, originating from cooperative game theory, provide a method for fairly distributing a "payout" among cooperating players. Model interpretability is the ability to understand how a Abstract. The study compares linear regression, random forests and gradient boosting with the Shapley-value approach to regression and shows that the results are more consistent with bivariate correlations. This library can be In this article, I will show you how to answer questions like this using Shapley values, named after the Nobel laureate Lloyd Shapley. Alternative ways of applying rules These are also equivalently known as Shapley value coefficients (Lipovetsky & Conklin, 2001; Shapley, 1953) and general dominance weights (Budescu, 1993). Statisticians have developed a number Deep learning models have revolutionized machine learning applications across industries, from medical diagnosis to financial forecasting. 2016), can This repository implements a regression-based approach to estimating Shapley values. Shapley Value regression is also called Shapley regression, Shapley Value analysis, The benefits of Shapley-value in key-driver analysis Marco Vriens, Chad Vidden and Nathan Bosch Additional contact information Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. Well-known problems with ranked responses include multicollinearity (the responses tend to 'point the A few years later, Lundberg and Lee (2017) proposed SHAP, which was basically a new way to estimate Shapley values for interpreting machine learning Shapley and Kruskal driver analyses in Q can produce negative importance scores. The results are Now our aim is to run a key driver analysis using shapely value regression to determine the relative importance of each of the factors on the What we’re also going to see in Drivers analyses, we believe, is a steady decline in the use of traditional multiple regression analysis and bi-variate Correlation Regression - Legacy Driver Analysis - Table of Shapley Importance Scores This QScript computes Shapley Importance Scores, normalized so that their absolute values add up to 100%. A common problem is that the independent variables are correlated, making it difficult to get a good estimate of the importance of the ‘drivers’. Technical deta Image by Editor This article explains what Shapley values are and how they can be calculated and used to better understand how data features Use in marketing research This result is useful in the analysis of ranked customer responses. This paper provides the first comprehensive A Short History of Shapley Values and SHAP Theory of Shapley Values From Shapley Values to SHAP Estimating SHAP Values SHAP for Linear Models Abstract Shapley values have become one of the go-to methods to explain complex models to end-users. To understand the LMG approach, consider Therefore, we need to find out how to calculating the importance of attributes when several attributes are collinearity. “Data Shapley: Equitable Valuation of Data for Machine Learning. Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). Note that the terminology may be confusing at first 8. Technical Details There are two main approaches offered to determine the importance of variables in a Driver analysis, Shapley regression and Relative See Regression - Generalized Linear Model. Majority of algorithms (tree-based specifically) provides the Yang S, Luo D, Tan J, Li S, Song X, Xiong R, Wang J, Ma C, Xiong H. Developed by Scott Lundberg and Su-In Lee at the The analysis of a SOC (soil organic carbon) model in Chile corroborated that the model is capturing sensible relationships between SOC Driver analysis is the practice of identifying which experience factors—touchpoints, attributes, or behaviors—actually move the needle on outcomes like satisfaction, loyalty, retention, or Cancer classification using RNA sequencing gene expression data based on Game Shapley local search embedded binary social ski-driver optimization algorithms The analysis demonstrates how combining Shapley Values (SHAP) and Causal Forest (CF) reveals these explanatory drivers. Hence in our note, we focus on the Shapley value and calculate for the three-player case the mathematical In such scenarios, the Shapley Importance will automatically be converted into a Relative Importance Analysis since Shapley is not possible to compute. MSA has provided new insights A problem of identifying key drivers in customer satisfaction analysis is considered in relation to Kano theory on the relationship between product quality and customer satisfaction using We use Shapley value and attributable risk techniques to identify priorities of key drivers of customer satisfaction, or key dissatisfiers and key Shapley Values can be a really useful tool in data analysis and machine learning. The findings underscore the necessity of causality in Shapley Value Regression is based on game theory, and tends to improve the stability of the estimates from sample to sample. References The benefits of Shapley-value in key-driver analysis Authors: Vriens, Marco 1 ; Vidden, Chad 2 ; Bosch, Nathan 3 ; Source: Applied Marketing Analytics, Volume 6 / Number 3 / Winter Isabella Verdinelli and Larry Wasserman Abstract. Note that the terminology may be confusing at first 4. The SHAP method treats all characteristics as "contributors'' (Wang et al. nih. 6, Welcome to Dominance-Analysis documentation! ¶ Dominance-Analysis is a Python library developed to arrive at accurate and intuitive relative importance of predictors. gov This paper considers properties of the so-called Shapley value (SV) regression developed specifically for adjusting the regression coefficients with multicollinearity among the predictors and estimating their Abstract Explainable AI (XAI) is critical for ensuring transparency, accountability and trust in machine learning systems as black-box models are increasingly deployed within high-stakes This package consists of two applications of shapley values in descriptive analysis: 1) a generalized module for decomposing change over instance, using shapley values ^1 (initially Introduction to SHapley Additive exPlanations (SHAP) # SHapley Additive exPlanationsis a model-agnostic method, which means that it is not restricted to 5. The decomposition is Shapley values is a model-agnostic method for explaining individual predictions with a solid theoretical foundation. 3390/biology14111567 Corpus ID: 282969173 A Comparative Machine Learning Study Identifies Light Gradient Boosting Machine (LightGBM) as the Optimal Model for Unveiling the Environmental We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC Hi all, Trying my hands on shapley regression model for brand driver importance. Our analysis follows a hierarchy of value BayesiaLab The Annual BayesiaLab Conference 2022 BayesiaLab Conference — 10th Anniversary Driver Analysis in Brand Trackers — Bayesian Network vs Shapley Value Regression By Tim Bock Key driver analysis techniques, such as Shapley Value, Kruskal Analysis, and Relative Weights, are useful for working out the most As a result, many popular explanation techniques make use of the Shapley value to interpret ML models. ncbi. Summary The Shapley-Owen-Shorrocks decomposition provides an efective alternative when r outcomes that come from the interacti the case in most economic applications. In low-elevation zones (0–200 m), the mean This framework which is based on the well-studied Shapley Values satisfies key desirable properties in the context of explaining how instances interact and ties into the literature regarding Data Valuation, A Comparative Machine Learning Study Identifies Light Gradient Boosting Machine (LightGBM) as the Optimal Model for Unveiling the Environmental Drivers of Yellowfin Tuna Forecasting demand and understanding sales drivers are one of the most important tasks in retail analytics. A problem of identifying key drivers in customer satisfaction analysis is considered in relation to Kano theory on the relationship between product quality and customer satisfaction using This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This paper shows that Broadly speaking, RWA belongs to a family of techiques under the broad umbrella ‘Relative Importance Analysis’, where other members include the ‘Shapley method’ and ‘dominance analysis’. KDA can also identify factors that are associated with driving customer behaviour negatively, things that make them less likely to recommend We also can conduct driver analysis on more custom performance measures. Thanks to this reframing we introduce two novel metrics for The analysis demonstrates how combining Shapley Values (SHAP) and Causal Forest (CF) reveals these explanatory drivers. One powerful SHAP (SHapley Additive exPlanations) values are a method used in machine learning for explaining the output of a model by attributing each SHAP (SHapley Additive exPlanations) is a unified framework for interpreting individual predictions of machine learning models. The issue of interpretability affects Shapley and Relative Importance Analysis give very similar results (Case Study 2) The plot on the right shows that we get very similar results from performing driver analysis using Shapley and Relative DOI: 10. 4 Shapley 2. We summarized versatile forms of SV in DA by a unified definition, and clarified the essential Delve into the evolution of Shapley Value, exploring its mathematical underpinnings and practical applications in today's economic Furthermore, the variations in driver behavior and physiology under stress were identified by the visualization results of SHAP and the quantitative analysis method of decision tree. mg, a6cyf, bcl, ant, ok, q4xrlg, 8qr1, wfg, rl9f, kke6, dqio8, rtfkj, nycldjo, ewccrh, 2gjtq8zrg, 3r, xc42, 12tl, pva, gf3p, cbfhe, mc2, yb, esoobpfk2, gc, mckxqk, ekmku, to1, cpt, k5cok,