This study primarily aimed to find out if machine learning classification algorithms could accurately classify L2 thesis statement writing performance as high or low using syntactic complexity indices. Secondarily, the study aimed to reveal how the syntactic complexity indices from which classification algorithms gained the largest amount of information interacted with L2 thesis statement writing performance. The data set of the study consisted of 137 high-performing and 69 low-performing thesis statements written by undergraduate learners of English in a foreign language context. Experiments revealed that the Locally Weighted Learning algorithm could classify L2 thesis statement writing performance with 75.61% accuracy, 20.01% above the baseline. Balancing the data set via Synthetic Minority Oversampling produced the same accuracy percentage with the Stochastic Gradient Descent algorithm, resulting in a slight increase in Kappa Statistic. In both imbalanced and balanced data sets, it was seen that the number of coordinate phrases, coordinate phrase per t-unit, coordinate phrase per clause and verb phrase per t-unit were the variables from which the classification algorithms gained the largest amount of information. Mann-Whitney U tests showed that the high-performing thesis statements had a larger amount of coordinate phrases and higher ratios of coordinate phrase per t-unit and coordinate phrase per clause. The verb phrase per t-unit ratio was seen to be lower in high-performing thesis statements than their low-performing counterparts.