The proposed hyper-curve fitting approach can be extended to handle datasets with a mix of linear and nonlinear predictors by incorporating a more flexible basis function representation. Instead of solely relying on polynomial basis functions, which may have limitations in capturing complex nonlinear relationships, a more diverse set of basis functions can be utilized. This can include functions such as trigonometric functions, exponential functions, or even piecewise functions to better capture the nonlinear dependencies present in the data. By expanding the basis function set, the model can adapt to a wider range of data patterns, allowing for a more accurate representation of both linear and nonlinear relationships within the dataset.