Individualization of head-related transfer functions (HRTFs) can improve the quality of binaural applications with respect to the localization accuracy, coloration, and other aspects. Using anthropometric features (AFs) of the head, neck, and pinna for individualization is a promising approach to avoid elaborate acoustic measurements or numerical simulations. Previous studies on HRTF individualization analyzed the link between AFs and technical HRTF features. However, the perceptual relevance of specific errors might not always be clear. Hence, the effects of AFs on perceived perceptual qualities with respect to the overall difference, coloration, and localization error are directly explored. To this end, a listening test was conducted in which subjects rated differences between their own HRTF and a set of nonindividual HRTFs. Based on these data, a machine learning model was developed to predict the perceived differences using ratios of a subject's individual AFs and those of presented nonindividual AFs. Results show that perceived differences can be predicted well and the HRTFs recommended by the models provide a clear improvement over generic or randomly selected HRTFs. In addition, the most relevant AFs for the prediction of each type of error were determined. The developed models are available under a free cultural license.