The potential limitations of the polynomial basis used in the inverse regression model include its inability to capture highly complex nonlinear relationships that may exist in the data. Polynomials have a fixed form and may struggle to represent intricate patterns that require more sophisticated functions. To address this limitation, alternative basis functions can be incorporated into the model. Functions like sine and cosine functions can capture periodic patterns, while exponential functions can model rapid growth or decay. Additionally, piecewise functions can be used to capture abrupt changes in the data. By incorporating a diverse set of basis functions, the model can better adapt to the nonlinearities present in the dataset, improving its overall performance and flexibility.