<ul data-eligibleForWebStory="false"><li>Machine learning methods have grown significantly, but their practical use in critical domains is hindered by their opacity.</li><li>Counterfactual explanations (CFEs) offer insights into altering decisions made by ML models, yet existing methods often require access to the model's training dataset.</li><li>A novel model-agnostic CFE method, NTD-CFE, based on reinforcement learning, is introduced to generate explanations without needing the training dataset.</li><li>NTD-CFE is designed for static and multivariate time-series datasets, reducing the search space and making CFEs more actionable by requiring fewer and smaller changes.</li></ul>

No $D_{\text{train}}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning

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