👉 Variance fumes refer to the phenomenon where small, seemingly insignificant changes in input data can lead to large and disproportionate variations in output results, particularly in statistical models or machine learning algorithms that rely on gradient descent or similar optimization techniques. This sensitivity to input variations makes variance fumes a critical consideration in model selection, data preprocessing, and algorithm design, as they can significantly impact the accuracy and reliability of predictions or inferences. Understanding and managing variance fumes is essential for building robust models that generalize well to new, unseen data.
