  We are totally immersed in the Big Data era and reliable algorithms and
methods for data classification are instrumental for astronomical research.
Random Forest and Support Vector Machines algorithms have become popular over
the last few years and they are widely used for different stellar
classification problems. In this article, we explore an alternative supervised
classification method scarcely exploited in astronomy, Logistic Regression,
that has been applied successfully in other scientific areas, particularly
biostatistics. We have applied this method in order to derive membership
probabilities for potential T Tauri star candidates from ultraviolet-infrared
colour-colour diagrams.
