Transformation Methods

Now we will discuss some of the common transformation methods below:

• Log Transformation: This transformation method uses logarithmic methods to reduce the impact of large values and transform exponential growth patterns. This method is particularly useful when we are dealing with variables that exhibit exponential growth like income or population size.
• Square Root Transformation: The square root transformation is employed to stabilize variances and address right-skewed distributions which is particularly useful when the variability of the target variable increases with its mean. By taking the square root, it compress the scale of larger values more than smaller ones, mitigating the impact of outliers and promoting a more homogenous spread of values.
• Quantile Transformation: Quantile transformation maps the data to a specified distribution, often a standard normal distribution. This transformation is useful for mitigating the impact of outliers and achieving a more uniform spread of values. It works by assigning a new value to each data point based on its rank order, ensuring that the transformed values follow the desired distribution. This transformation is particularly effective when the target variable has a non-normal distribution or contains extreme values.
• Box-Cox Transformation: The Box-Cox transformation is a complex method which encompasses both logarithmic and power transformations. It is designed to handle a variety of distributions and adapt to the underlying structure of the data. The Box-Cox transformation is suitable for dealing with skewness, heteroscedasticity and other distributional issues.

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.