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With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. To this end, we built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters AV and Teff, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars. [ABSTRACT FROM AUTHOR]

Understanding the formation and evolution of our Galaxy requires accurate distances, ages, and chemistry for large populations of field stars. Here, we present several updates to our spectrophotometric distance code, which can now also be used to estimate ages, masses, and extinctions for individual stars. Given a set of measured spectrophotometric parameters, we calculate the posterior probability distribution over a given grid of stellar evolutionary models, using flexible Galactic stellar-population priors. The code (called StarHorse) can accommodate different observational data sets, prior options, partially missing data, and the inclusion of parallax information into the estimated probabilities. We validate the code using a variety of simulated stars as well as real stars with parameters determined from asteroseismology, eclipsing binaries, and isochrone fits to star clusters. Our main goal in this validation process is to test the applicability of the code to field stars with known Gaia-like parallaxes. The typical internal precisions (obtained from realistic simulations of an APOGEE+Gaia-like sample) are ≃8 per cent in distance, ≃20 per cent in age, ≃6 per cent in mass, and ≃0.04 mag in AV. The median external precision (derived from comparisons with earlier work for real stars) varies with the sample used, but lies in the range of ≃[0, 2] per cent for distances, ≃[12, 31] per cent for ages, ≃[4, 12] per cent for masses, and ≃ 0.07 mag for AV. We provide StarHorse distances and extinctions for the APOGEE DR14, RAVE DR5, GES DR3, and GALAH DR1 catalogues. [ABSTRACT FROM AUTHOR]