Astraea is a package to train Random Forest (RF) models on datasets. It provides tools to train RF classifiers and regressors as well as perform simple cross-validation tests and create performance plots on the test set.
It was first developed to calculate rotation period of stars from various stellar properties provided and was intended to predict long rotation periods (e.g. those of M-dwarfs) from short TESS lightcurves (27-day lightcurves).
We provide access to trained models on stars from the catalog by McQuillan et al. (2014), Garcia et al. (2014), and Santos et al. (2019). User can predict whether the rotation period can be recovered and measure recoverable rotation periods for the stars in the Kepler field by using their temperatures, colors, kinematics, etc.
For user guides and tutorials, see Astraea readthedocs
Original paper can be found here: https://ui.adsabs.harvard.edu/abs/2020AJ....160..168L/abstract.