Auditory-perceptual assessment is widely used in clinical and pedagogical practice for speech and singing voice, yet several studies have shown poor intra- and inter-rater reliability in both clinical and singing voice contexts. Recent advances in artificial intelligence and machine learning offer models for automated classification and have demonstrated discriminatory power in both pathological and healthy voice. This study develops and tests an XGBoost decision tree based machine learning classifier to develop automated vocal mode classification in healthy singing voice. Classification models trained on mel-frequency cepstrum coefficients, MFCC-Zero-Time Windowing, glottal features, voice quality features, and α-ratios demonstrated 92% average F1-score accuracy in distinguishing metallic and non-metallic singing for male singers and 87% average F1-score for female singers. The model distinguished vocal modes with 70% and 69% average F1-score for male and female samples, respectively. Model performance was compared to human auditory-perceptual assessments of 64 corresponding samples performed by 41 professional singers. The model performed with approximating or subpar performance to human assessors on task-matched problems. The XGBoost gains observed across tested features reveal that the most important attributes for the tested classification problems were MFCCs and α-ratios between high and low frequency energy, with models trained on only these features achieving performance not statistically significantly different from the best tested models. The best automated models in this study do not yet match human auditory-perceptual discrimination but improve on previously reported F1-average accuracies in automated classification in singing voice.