Dopamine plays a crucial role in the neural mechanisms underlying behavior, yet quantifying its impact through genetic modifications presents significant challenges, particularly due to the high-dimensional nature of the resulting data. In this study, we address these challenges by proposing a novel methodological framework using the Random Forest algorithm, tailored for robust multivariate analysis without the typical assumptions required in conventional statistical testing. Our experimental approach involved detailed video recordings of Drosophila melanogaster larvae, focusing on the effects of alterations to dopaminergic neurons on larval movement patterns. Data analysis was conducted using IMBAtracker[1], which generated comprehensive time-series data detailing the positional dynamics of the subjects. We categorized the resultant IMBA tracker's attributes into two primary groups: speed-related attributes (12 attributes) and bending- and head-cast-related behavioral attributes (14 attributes). To manipulate dopamine system function, we employed both genetic and pharmacological interventions, activating TH-GAL4 neurons optogenetically, employing RNA interference targeting tyrosine hydroxylase (TH), and administering treatments with 3-Iodo-L-tyrosine (3IY)[2], a dopamine synthesis inhibitor, and L-DOPA, a dopamine precursor. Our analytical pipeline is structured to select and compare two distinct groups in each iteration, applying the Random Forest algorithm to determine significant differences across various attributes. The key outputs from this analysis include: 1) the accuracy of class prediction for test data, which quantifies the distinctiveness between the two groups and assesses the effectiveness of the genetic modifications based on either all or a subset of attributes; 2) characterizing the importance scores of each attribute, which elucidates the most influential factors affecting larval behavior and guides further detailed investigations. Application of our method to neural and behavioral data suggests that while dopamine system modulations can significantly alter larval behavior, other factors including light stimulation and possible co-transmitters released from dopamine neurons contribute to the observed phenotypes. This comprehensive analysis not only confirms the pivotal role of dopamine but also enhances our understanding of the complex interactions of dopaminergic neurons within neural circuits.