The marble burying test is a commonly used paradigm to screen phenotypes in mouse models of neurodevelopmental and psychiatric disorders. The current methodological approach relies solely on reporting the number of buried marbles at the end of the test. By measuring the proxy of the behavior (buried marbles), rather than the behavior itself (burying bouts), many important characteristics regarding the temporal aspect of this assay are lost. Here we introduce a novel, automated method to quantify mouse behavior throughout the duration of the marble burying test with the focus on the burying bouts. Using open-source software packages, we trained a supervised machine learning algorithm (the classifier) to distinguish burying behavior in freely moving mice. In order to confirm the classifier's accuracy and uncover the behavioral meaning of the marble burying test, we performed marble burying test in three mouse models: Ube3am-/p+ (Angelman Syndrome model), Shank2-/- (autism model), and Sapap3-/- (obsessive-compulsive disorder model) mice. The classifier scored burying behavior accurately and consistent with the literature in the Ube3am-/p+ mice, which showed decreased levels of burying compared to controls. Shank2-/- mice showed a similar pattern of decreased burying behavior, which was not found in Sapap3-/- mice. Tracking mouse behavior throughout the test enabled us to quantify activity characteristics, revealing hypoactivity in Ube3am-/p+ and hyperactivity in the Shank2-/- mice, indicating that mouse activity is unrelated to burying behavior. Together, we demonstrate that our classifier is an accurate method for the analysis of the marble burying test, providing more information than the currently used methods.