Thin features (thin walls and thin struts) in the strut-based and planar lattice structure show a powder feedstock-geometry-process-quality (PGPQ) characteristic. However, the relationship between the input variables (powder feedstock, laser Power, scan speed, feature type, and feature dimension) and their properties are complex to understand. Besides input variables, the intermediate variables such as dimension (dimensional variability within a sample), porosity and pore size distribution, and even the grain sizes are expected to correlate to the quality/mechanical properties of these thin features. Therefore, the main objective of this study is to explore the complex graphical relationship between the input variables, intermediate variables, and the final flexural properties of thin features by utilizing a graphical model-based machine learning (ML) model. The ML model depicts the influence of some of the intermediate variables (e.g. porosity) on the flexural properties of the thin features, which helps revealing the complex PGPQ characteristics.