Shapley variable importance cloud for machine learning models

• URL: http://arxiv.org/abs/2212.08370v1
• Date: Fri, 16 Dec 2022 09:45:22 GMT
• Title: Shapley variable importance cloud for machine learning models
• Authors: Yilin Ning, Mingxuan Liu, Nan Liu
• Abstract summary: Recently developed Shapley variable importance cloud (ShapleyVIC) provides comprehensive and robust variable importance assessments. benefits of ShapleyVIC inference have been demonstrated in real-life prediction tasks. ShapleyVIC implementation for machine learning models to enable wider applications.
• Score: 4.1359299555083595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e.g., by using the Shapley additive explanations (SHAP) method. The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to provide comprehensive and robust variable importance assessments, with estimated uncertainty intervals for a more complete understanding of variable contributions to predictions. ShapleyVIC was initially developed for applications with traditional regression models, and the benefits of ShapleyVIC inference have been demonstrated in real-life prediction tasks using the logistic regression model. However, as a model-agnostic approach, ShapleyVIC application is not limited to such scenarios. In this work, we extend ShapleyVIC implementation for machine learning models to enable wider applications, and propose it as a useful complement to the current SHAP analysis to enable more trustworthy applications of these black-box models.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Shapley Marginal Surplus for Strong Models [0.9831489366502301]
  We show that while Shapley values might be accurate explainers of model predictions, machine learning models themselves are often poor explainers of the true data-generating process (DGP) We introduce a novel variable importance algorithm, Shapley Marginal Surplus for Strong Models, that samples the space of possible models to come up with an inferential measure of feature importance.
  arXiv  Detail & Related papers  (2024-08-16T17:06:07Z)
• Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences [6.067007470552307]
  We propose a methodology for finding sequences of machine learning models that are stable across retraining iterations. We develop a mixed-integer optimization formulation that is guaranteed to recover optimal models. Our method shows stronger stability than greedily trained models with a small, controllable sacrifice in predictive power.
  arXiv  Detail & Related papers  (2024-03-28T22:45:38Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Stabilizing Estimates of Shapley Values with Control Variates [3.8642937395065124]
  Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models. Our methodology is applicable to any machine learning model and requires virtually no extra computation or modeling effort. On several high-dimensional datasets, we find it can produce dramatic reductions in the Monte Carlo variability of Shapley estimates.
  arXiv  Detail & Related papers  (2023-10-11T17:18:51Z)
• ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse [59.500060790983994]
  This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference.
  arXiv  Detail & Related papers  (2023-08-17T19:12:13Z)
• Consistent Counterfactuals for Deep Models [25.1271020453651]
  Counterfactual examples are used to explain predictions of machine learning models in key areas such as finance and medical diagnosis. This paper studies the consistency of model prediction on counterfactual examples in deep networks under small changes to initial training conditions.
  arXiv  Detail & Related papers  (2021-10-06T23:48:55Z)
• Shapley variable importance clouds for interpretable machine learning [2.830197032154301]
  We propose a Shapley variable importance cloud that pools information across good models to avoid biased assessments in SHAP analyses of final models. We demonstrate the additional insights gain compared to conventional explanations and Dong and Rudin's method using criminal justice and electronic medical records data.
  arXiv  Detail & Related papers  (2021-10-06T03:41:04Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
  It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
  arXiv  Detail & Related papers  (2020-07-15T07:07:07Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.