Fuzzy Inference Systems (FIS) effectively model and reason with complex and uncertain information in an interpretable, understandable, and transparent way. Takagi-Sugeno-Kang (TSK) is one of the most widespread types of FIS, appreciated for its ability to output crisp values by leveraging linear models as consequents. In this tribute to Prof. Sugeno, we discuss our previous works based on TSK inference. In particular, we focus on pyFUME, a Python library for automatically estimating first-order TSK FIS from data. We introduce a relevant advancement in pyFUME, i.e., the ability to handle categorical variables within the consequents, which significantly enhances the model’s performance in regression and classification tasks. This improved version completes the foundation for pyFUME to handle mixed-type data. Our results on three diverse datasets show the capabilities of our method, which performs better than the previous implementation.