This research encompasses a thorough examination of 5595 confirmed exoplanets listed in the Archive as of 10 March 2024, systematically evaluated according to their calculated average surface temperatures and stellar classifications of their host stars, taking into account the biases implicit in the methodologies used for their discovery. Machine learning, in the form of a Random Forest classifier and an XGBoost classifier, is applied in the classification with high accuracies. The feature importance analysis indicates that our approach captures the most important parameters for habitability classification.

it suggests the goal for our action should be sustainability – an anthropocentric, global concept – rather than habitability – a multispecies, planetary concept.

Before the publication of the ‘Gang of Four’ book that popularised software patterns [4], Richard Gabriel described Christopher Alexander’s patterns in 1993 as a basis for reusable object‐oriented software in the following way:Habitabilityisthecharacteristicofsourcecodethatenablesprogrammers,coders,bug­fixers,andpeoplecomingtothecodelaterinitslifetounderstanditsconstructionandintentionsandtochangeitcomfortablyandconfidently.