Ancestry assessment using random forest modeling.

A skeletal assessment of ancestry relies on morphoscopic traits and skeletal measurements. Using a sample of American Black (n = 38), American White (n = 39), and Southwest Hispanics (n = 72), the present study investigates whether these data provide similar biological information and combines both data types into a single classification using a random forest model (RFM). Our results indicate that both data types provide similar information concerning the relationships among population groups. Also, by combining both in an RFM, the correct allocation of ancestry for an unknown cranium increases. The distribution of cross-validated grouped cases correctly classified using discriminant analyses and RFMs ranges between 75.4% (discriminant function analysis, morphoscopic data only) and 89.6% (RFM). Unlike the traditional, experience-based approach using morphoscopic traits, the inclusion of both data types in a single analysis is a quantifiable approach accounting for more variation within and between groups, reducing misclassification rates, and capturing aspects of cranial shape, size, and morphology.