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 ($R^2$), 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 a