Code from: Machine learning can accurately assign fossil and extant species to crown toxicoferan (Reptilia: Squamata) groups using inner ear shape data

Because the inner ear is involved in gaze stabilization, balance, and
hearing, fossil inner ear endocast morphology has been used to infer the
palaeoecology of extinct species. These results have been used to inform
major evolutionary transitions, including the ecological origin of snakes.
However, prior studies found only modest correlations between inner ear
shape and ecological traits, and did not apply machine learning
approaches, which could potentially reveal greater predictive
relationships between inner ear morphology and ecology. Here, we combine
three-dimensional geometric morphometrics with machine learning to
evaluate the performance of inner ear morphology as a predictor of habitat
use and phylogenetic affinities across a broad sample of toxicoferans
(snakes, anguimorphs, and iguanians) representing 73 extant species and 4
fossil species. We find a weak correlation between habitat and inner ear
morphology, but machine learning models cannot accurately predict habitat
preference in extant species (44% accuracy). In contrast, we find a
strongly predictive relationship (95% accuracy) between inner ear shape
and higher-order classification. Our results demonstrate that the inner
ear shape data we measured strongly predict evolutionary classifications
rather than habitat use in crown toxicoferan squamates. We conclude that
machine learning provides a versatile analytical approach to the
reconstruction of palaeobiology.