VizieR Online Data Catalog: Machine learning metallicity predictions using SDSS (Miller, 2015)

Photometric colors and spectroscopic [Fe/H] measurements for the training set sources are selected from SDSS data release 10 (DR10; Ahn et al. 2014ApJS..211...17A). The selection criteria are designed to select sources with the most reliable photometric and spectroscopic measurements. It is important to note that each of these criteria can be applied to the ~2.6x10⁸ SDSS stars with no spectroscopic observations, ensuring that these choices do not introduce a significant bias in the final model predictions. See section 2 for further explanations.

In addition to building a robust and representative training set, the choice of machine-learning algorithm is essential for the construction of a useful model. Three different algorithms are utilized in this study: the K-nearest Neighbors (KNN) regression, the Random Forest (RF) method and the Suport Vector Machines (SVMs) model. See section 3 for further explanations.

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