Rodríguez Rodríguez, Álvaro ManuelAndrés Núñez, Eva MaríaMarín Díaz, Gabriel2026-10-262026-10-262026Rodríguez-Rodríguez, Á. M., Núñez, E. A., & Díaz, G. M. (2026). Interpretability as a leakage detector: A case study in machine-learning-based exoplanet detection with TESS. Machine Learning: Science and Technology, 7(5), 055048. https://doi.org/10.1088/2632-2153/aeb3ca2632-2153https://hdl.handle.net/11268/17511The Transiting Exoplanet Survey Satellite (TESS) generates photometric time series for hundreds of thousands of stars, far more than can be vetted by hand, motivating machine-learning (ML) triage. We present a low-resource, interpretable pipeline that classifies TESS light curves as transit-bearing or not using a random forest trained on engineered summary features, treating feature-importance analysis as a formal auditing step, and apply it to Sector 68. The positive class comprises TESS-confirmed planet hosts; the negative class comprises Sector 68 targets with no catalogued planet or candidate, assembled from a different source.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Computación y tecnologíajournal article10.1088/2632-2153/aeb3caopen accessLógica matemáticaInteligencia artificialAstronomíaGoal 4: Quality educationGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation