Data potential and feasibility study with Grid Mean Algorithm

The Grid Mean Algorithm is a computational approach designed to evaluate regression metrics such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) directly on tabular data without the need to train machine learning (ML) models. This method enables researchers and practitioners to assess the potential of data for regression tasks, estimate the feasibility of ML projects, and make informed decisions about resource allocation. Additionally, the algorithm allows for estimating the approximate accuracy limit achievable with the given data, making it a valuable criterion for determining the optimality of a model. By addressing whether further research stages are necessary or redundant, it provides a practical tool for planning ML experiments and evaluating the economic viability of investing in such models. Experiments on synthetic datasets demonstrate the method's capability to produce accurate metric estimates across various functional forms and noise levels, making it a robust choice for initial data exploration and ML project planning.