The purpose of this study was to build a Random Forest supervised machine learning model in order to predict musical rater-type classifications based upon a Rasch analysis of raters' differential severity/leniency related to item use. Raw scores (N = 1,704) from 142 raters across nine high school solo and ensemble festivals (grades 9-12) were collected using a 29-item Likert-type rating scale embedded within five domains (tone/intonation, n = 6; balance, n = 5; interpretation, n = 6; rhythm, n = 6; and technical accuracy, n = 6). Data were analyzed using a Many Facets Rasch Partial Credit Model. An a priori k-means cluster analysis of 29 differential rater functioning indices produced a discrete feature vector that classified raters into one of three distinct rater-types: (a) syntactical rater-type, (b) expressive rater-type, or (c) mental representation rater-type. Results of the initial Random Forest model resulted in an out-of-bag error rate of 5.05%, indicating that approximately 95% of the raters were correctly classified. After tuning a set of three hyperparameters (n[subscript tree], m[subscript try], and node size), the optimized model demonstrated an improved out-of-bag error rate of 2.02%. Implications for improvements in assessment, research, and rater training in the field of music education are discussed.