G2None

G2None is a machine learning technique used to identify the most relevant features or variables in a dataset for making predictions. It combines genetic algorithms—methods inspired by natural evolution—to efficiently explore possible feature combinations, with a deep learning model that evaluates how well these features predict outcomes. The goal is to select the best features that improve model accuracy while reducing complexity, leading to more efficient and reliable predictions. Essentially, G2None helps in pinpointing key factors in data, making models smarter and more focused without unnecessary information.