The Box-Cox transformation is a generalized “power transformation” that transforms data to make the distribution more normal.
For example, when its lambda parameter is 0, it’s equivalent to the log-transformation.
It’s used to stabilize the variance (eliminate heteroskedasticity) and normalize the distribution.
The Box-Cox transformation is a statistical technique used primarily in data preprocessing for normalizing data. It’s particularly useful when dealing with skewed data distributions.
The main purpose of the Box-Cox transformation is to stabilize the variance and make the data more normally distributed. This can be beneficial for improving the performance of certain statistical models that assume normality or have homoscedasticity (constant variance) assumptions.
In a Machine Learning context, the Box-Cox transformation might be applied to features or target variables to meet the assumptions of linear regression, or it could be used as a preprocessing step to improve the performance of various models, such as those based on decision trees, support vector machines, or neural networks.
In summary, the Box-Cox transformation is used to normalize data and stabilize variance, making it a valuable tool in data preprocessing for Machine Learning tasks, especially when dealing with skewed distributions or when the assumptions of the chosen model require normally distributed data.