AbstractFeature interaction is a crucial concept when it comes to machine learning interpretability. These interactions make it easier for the person using a machine learning tool to know how the model made its prediction. Intrinsic models such as the Generalized linear models pull their interpretability aspects from detecting feature interactions on the data. However, these models do not search the whole sample space for interactions and assume all interactions to the target feature are identical. A hybrid model combining Rough Set and Generalized linear models is proposed. Rough Set uses the concept of information granulation and approximations of regions with meaningful information to find feature interactions in the whole sample space. The detected features will then be modeled onto a Generalized linear model for prediction purposes. The dataset used in the experiment was drawn from wunderground.com online weather site, specifically the Kariki Farm online weather station. The proposed methodology for the research is the CRISP-DM method.Keywords: Machine interpretability, Rough Set, Generalized linear model, weather prediction.

Seeing that coding React-JS can be repetitive and prone to errors like most other programming languages, which makes it a daunting task, Automatic Code Generation (ACG) tools exist to help tackle this. The current model used in most of the ACG tools for React-JS comes up with components which is a fraction of the entire application when it comes to coming up with the entire Create, Retrieve, Update and Delete (CRUD) applications. Developers still need to come up with the logic of how CRUD functionalities will be implemented in the code that was generated by these models. This study seeks to address this gap by proposing an enhanced automatic generating model for Single Page Applications (SPA) in React-JS coined CRUD-Bolt. The study employs the USE questionnaire to test its usability whereby 38 React-JS developers across the globe fill the questionnaire after testing it. The results are imported into Statistical Package for the Social Sciences (SPSS) to evaluate CRUD-Bolt. The outcome of the study is threefold: achieve an enhanced model coined CRUD-Bolt, make it available to developers, and test its usability via SPSS.