However, the effects of these three parameters (i.e.,  , T and  ) on  still lacked direct quantitative comparisons, and in particular, the effects of T and  are difficult to compare. We further calculated the R 2 scores in each model shown in Figure . The results have a similar trend with MSE error: XGBoost has the highest R 2 score of 0.994. These two results demonstrate the success of XGBoost in the prediction of viscosity for PNCs, which is is significant for designing the process in theory for processing processes in complex environments. Therefore, we chose XGBoost as the final model for the prediction on the testing set. The relatively large prediction error of the LR model is related to the nonlinear rheological properties for PNCs, while the LR model is more suitable for analyzing linear data. The linearity between  and  ,  and T data is low: the Pearson correlation coefficnets are 0.095, 0.107, and -0.431 for  ,  and T , respectively. It is well-known that most PNC fluids are typical of complex fluids, PNCs with various  commonly exhibits shear thinning behavior under shear flow in different  and T . The  -dependence of  shows a crossover at critical  , first is a  ( or T ) -dependent Newtonian plateau at small  then the  decrease with increasing , generally described by - (  is the power law exponent, 0<  <1) .