Results 11 to 20 of about 5,778,654 (336)
Decision trees in epidemiological research
Emerging Themes in Epidemiology, 2017 Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored ...
Ashwini Venkatasubramaniam +5 more
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On Tackling Explanation Redundancy in Decision Trees [PDF]
Journal of Artificial Intelligence Research, 2022 Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals
Yacine Izza +2 more
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On Computing Probabilistic Explanations for Decision Trees [PDF]
Neural Information Processing Systems, 2022 Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision ...
M. Arenas +3 more
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Large Scale Prediction with Decision Trees [PDF]
Journal of the American Statistical Association, 2021 This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of predictor variables grows sub-exponentially with the ...
Jason M. Klusowski, Peter M. Tian
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Multivariate decision trees [PDF]
Machine Learning, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brodley, Carla E., Utgoff, Paul E.
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Machine Learning, 1993 Quinlan and Rivest have suggested a decision-tree inference method using the Minimum Description Length idea. We show that there is an error in their derivation of message lengths, which fortunately has no effect on the final inference. We further suggest two improvements to their coding techniques, one removing an inefficiency in the description of ...
Wallace, C. S., Patrick, J. D.
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Predicting Credit Scores with Boosted Decision Trees
Forecasting, 2022 Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a ...
João A. Bastos
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Constraint Enforcement on Decision Trees: A Survey
ACM Computing Surveys, 2022 Decision trees have the particularity of being machine learning models that are visually easy to interpret and understand. Therefore, they are primarily suited for sensitive domains like medical diagnosis, where decisions need to be explainable. However,
Géraldin Nanfack +2 more
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Evolutionary Learning of Interpretable Decision Trees
IEEE Access, 2023 In the last decade, reinforcement learning (RL) has been used to solve several tasks with human-level performance. However, there is a growing demand for interpretable RL, i.e., there is the need to understand how a RL agent works and the rationale of ...
Leonardo L. Custode, Giovanni Iacca
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Decision trees for regular factorial languages
Array, 2022 In this paper, we study arbitrary regular factorial languages over a finite alphabet Σ. For the set of words L(n)of the length n belonging to a regular factorial language L, we investigate the depth of decision trees solving the recognition and the ...
Mikhail Moshkov
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