SAILS is a surrogate GAM framework that analyzes local effects to detect, categorize, and visualize the functional forms of pairwise interactions in black-box ML models.
A Simple and Effective Model-Based Variable Importance Measure
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In the era of "big data", it is becoming more of a challenge to not only build state-of-the-art predictive models, but also gain an understanding of what's really going on in the data. For example, it is often of interest to know which, if any, of the predictors in a fitted model are relatively influential on the predicted outcome. Some modern algorithms---like random forests and gradient boosted decision trees---have a natural way of quantifying the importance or relative influence of each feature. Other algorithms---like naive Bayes classifiers and support vector machines---are not capable of doing so and model-free approaches are generally used to measure each predictor's importance. In this paper, we propose a standardized, model-based approach to measuring predictor importance across the growing spectrum of supervised learning algorithms. Our proposed method is illustrated through both simulated and real data examples. The R code to reproduce all of the figures in this paper is available in the supplementary materials.
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UNVERDICTED 3representative citing papers
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.
A literature survey reviewing traditional diagnostics, AI-driven studies, and explainable AI models for mental disorder detection via online social media, including datasets, evaluation practices, issues, and future directions.
citing papers explorer
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SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
SAILS is a surrogate GAM framework that analyzes local effects to detect, categorize, and visualize the functional forms of pairwise interactions in black-box ML models.
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Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
This survey synthesizes XAI methods with surrogate modeling workflows for simulations and outlines a research agenda to embed explainability into simulation-driven design and decision-making.
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Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook
A literature survey reviewing traditional diagnostics, AI-driven studies, and explainable AI models for mental disorder detection via online social media, including datasets, evaluation practices, issues, and future directions.