Y-Transform refers to a mathematical transformation technique applied to the output variable (Y) in machine learning and statistics. This transformation can help stabilize variance, improve normality, or linearize relationships in the data, making it more suitable for modeling. Common forms of Y-Transform include logarithmic transformations, square root transformations, or Box-Cox transformations. By adjusting the output variable, practitioners can enhance the effectiveness of predictive models and improve their ability to capture complex relationships within the data. Y-Transform is especially beneficial in regression analysis, where assumptions about the distribution of the response variable significantly impact model performance.