What transformation method raises each data example to a power of some lambda value to reduce skewness?

The Box-Cox transformation is a family of power transformations designed to stabilize variance and make the data more closely resemble a normal distribution. This transformation is particularly useful when dealing with skewed data, as it can effectively reduce skewness by raising the data to a power defined by a lambda (λ) value.

The Box-Cox transformation is specifically defined for positive values, allowing the lambda parameter to vary. When λ = 0, the transformation corresponds to taking the logarithm of the data; for other values of λ, the data is raised to the power of λ. By optimizing the choice of λ, practitioners can find the best transformation to make the data more normally distributed and meet the assumptions of many statistical techniques.

In contrast, while other transformations such as log transformations can also help reduce skewness, they are not as flexible as the Box-Cox transformation in terms of accommodating negative values or providing various ways of addressing skewness based on the chosen λ. Z-score normalization standardizes data but does not change the distribution shape, and the Yeo-Johnson transformation is often used for similar purposes but is meant to accommodate both positive and negative values without a specified λ transformation.