Fig. 7: Illustration of unrealistic concrete mixtures generated by permutation feature importance method.
From: Machine learning in concrete science: applications, challenges, and best practices
Data were obtained from the concrete compressive strength dataset available in the UC Irvine Machine Learning Repository24,112 (Table 2). a, b The variable for water (a) or cement (b) was randomly shuffled while keeping other variables constant. The black circles refer to the observed mixtures, the red pluses to the generated mixtures after shuffling, and their transparency represents the frequency. SCMs, supplementary cementitious materials.
