Random Forest model was built to predict the ∆W using the complete dataframe where the extrusion of the failed prints were included as ∆W = -1.0. Figure shows the graphical assessment of predictive model for ∆W . The performance metrics of the ∆W model presented in Furthermore, the examination of feature importance (Fig. ) exhibited a considerable shift following the exclusion of failed printings. The variable rheological loss factor, tanδ, and printing parameter temperature, T, emerged as the most influential elements on the objective both having 17% of importance. The flow consistency index and the extrusion flow were the second and third most influential parameters on the predictive model.