Which transformation is often applied to variables with extreme values to improve model accuracy?

The logarithmic transformation is often applied to variables with extreme values, also known as outliers, because it can help to stabilize variance and normalize the distribution of the data. When extreme values are present, they can disproportionately influence model outcomes and lead to less accurate predictions. By applying a logarithmic transformation, these extreme values are compressed, which reduces their impact on the model.

This transformation is particularly useful when the data spans several orders of magnitude, as it helps in bringing all values closer together, thus improving the overall fit of the model. The logarithmic transformation typically converts multiplicative relationships into additive ones, making it easier to interpret and analyze the effects of independent variables on the dependent variable.

Normalization and standardization, on the other hand, focus on scaling the data, while polynomial transformations involve creating additional features through the raising of existing features to a power, which may not directly address issues of extreme values.