Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Occupational Employment Survey datasets were obtained from years 1997 (OES97) and 2010 (OES10) of the annual Occupational Employment Survey compiled by the US Bureau of Labor Statistics. Each row provides the estimated number of full-time equivalent employees across many employment types for a specific metropolitan area. There are 334 and 403 cities in the 1997 and 2010 datasets, respectively. The input variables in these datasets are a randomly sequenced subset of employment types (e.g. doctor, dentist, car repair technician, etc.) observed in at least 50 % of the cities (some categories had no values for particular cities). The targets for both years are randomly selected from the entire set of categories above the 50 % threshold. Missing values in both the input and the target variables were replaced by sample means for these results. To our knowledge, this is the first use of the OES dataset for benchmarking of multi-target prediction algorithms.