Why Transform Targets?

We need to perform target variable transformations in real-world regression-based regression datasets to address issues like non-linearity, heteroscedasticity, and skewed distributions. These complex patterns can’t be handled by linear and low-standard tree-based regression models as they blindly assume a linear relationship between predictors and the target variable. Transformation can help to mitigate these issues and improve the model’s ability to capture complex patterns. Some of the key benefits of transforming targets for regression problems are listed below:

1. Normalization of Distributions: Transformation can convert skewed or non-normal distributions into more symmetric, normal-like distributions. This is particularly beneficial as it aligns with the assumption of normality in many statistical models.
2. Stabilization of Variances: Heteroscedasticity, where the spread of residuals varies across the range of predictors, can be addressed by transforming the target variable which helps to stabilize variances and ensures that model predictions are more consistent across the entire data range.
3. Handling Non-linearity: If the relationship between predictors and the target is nonlinear, certain transformations (e.g., logarithmic or power transformations) can help capture these nonlinear patterns which makes the relationship more amenable to regression modeling.
4. Improved Model Performance: Transformation makes betterment of model performance by facilitating a more accurate representation of underlying patterns and reducing the impact of outliers.

Regression modelling plays a crucial role in predicting numerical outcomes and understanding the relationships between variables. One key aspect of building robust regression models is the careful consideration of the target variable, as its distribution and characteristics can significantly impact model performance. In this article, we will discuss the effect of transforming the targets in regression modelling and their benefits.