This study compares classification and observation training in categorization tasks involving multiple rules, including an optimal XOR rule and suboptimal uni-dimensional rules. Participants (N = 192) were assigned to either condition, with classification involving active categorization and feedback, while observation involved studying the pair of category label and item together. Results showed that classification participants outperformed observation participants in accuracy and exhibited greater consistency in strategy use. Bayesian modeling revealed no significant difference in rule adoption between conditions, but classification led to fewer strategy switches and lower error rates. These findings suggest that classification training enhances performance by fostering stronger commitment to adopted strategies. The study highlights the importance of strategy commitment in categorization and questions the reliance on overall accuracy alone as a performance metric.