The concept of fuzzy set inclusion is essential in decision-making, classification, and natural language processing, yet comparing different inclusion measures remains a significant challenge. To address this, we propose a visualization method using contour plots to facilitate direct comparison of different inclusion measures. By analyzing rescaled fuzzy sets, we investigate how inclusion measures behave under various parameter settings. Several well-known measures, including those based on S- and R-implications, are examined. Understanding these computational and theoretical properties is critical for selecting efficient inclusion measures in practical applications, where optimizing model performance. This study contributes to the development of systematic approaches for evaluating inclusion measures.