CSENone

CSENone is a concept used in machine learning to identify features or variables that do not significantly contribute to a model's predictive performance. It helps distinguish which data points or features can be considered irrelevant or unimportant, allowing for more efficient and accurate models. Think of it as a way to filter out noise or unnecessary information, focusing only on the most meaningful data to improve decision-making and reduce complexity. This improves the model's clarity, speed, and reliability in analyzing data and making predictions.