Supervised Machine Learning Approach to Iron Meteorite Classification Using Random Forest: A Predictive Model to Classify Ungrouped Irons

Abstract Iron meteorites provide insight into the formation processes of planetary cores from the early Solar System. Through further classifying individual irons into specific groups, which are assumed to represent a common parent body, greater information can be yielded into the formation processes of those bodies. The current classification system of iron meteorites includes 13 groups distinguished by trace element compositions and mineralogical properties (i.e., bandwidth size of the α‐Fe,Ni‐alloy, silicate inclusions, etc.). Group sizes vary with only 6 members identified as IIG irons and 391 members in the largest group, the IAB‐complex. One‐hundred and fifty‐nine irons (or >11% of all iron meteorites) that cannot be grouped with the current classification scheme are labeled “ungrouped.” This research explores whether supervised machine learning (ML) algorithms offer insights into classification of the ungrouped irons, despite the heavily imbalanced group data used for algorithm training. A Random Forest classifier is presented in this work that achieved weighted F1‐scores of ∼91–97% of testing data sets. This outcome indicated that ML is possible for the iron meteorite data set. The accuracy of the model ranges from ∼90–98%. As a result, at least 59 ungrouped irons could be reclassified to preexisting iron groups, 16 may belong to small grouplets while 12 should remain ungrouped. This work further discusses the implications of these potential reclassifications and how ML may be applicable to other meteorite studies.