👉 Unrefining is a process in which the essence or principal component of a dataset is extracted from it, leaving out all other components. This is often used in machine learning and data analysis to identify the most important features or attributes that contribute to the outcome of a model. In other words, unrefining involves removing irrelevant or redundant information from a dataset, leaving behind only the core information that matters most. This can be useful when dealing with datasets that are too large or complex for traditional statistical methods
