What impact do transformations of input variables have in logistic regression?

Transformations of input variables in logistic regression can significantly enhance model performance. By transforming variables, you can help meet the assumptions of the logistic regression model, such as linearity in predictors for the log-odds of the outcome. For instance, logarithmic transformations can mitigate the effects of outliers or skewness, leading to a more normal distribution of the variables, which improves the model's fit. Furthermore, transforming variables can help capture non-linear relationships that may exist between predictors and the response variable, giving the model a better chance to uncover complex patterns in the data.

The impact of these transformations can lead to improved predictive accuracy, better model diagnostics, and more reliable parameter estimates. By addressing issues such as multicollinearity or non-linearity, transformations can create a more robust logistic regression model. Therefore, it is reasonable to assert that transformations are likely to increase the performance of logistic regression, making it a strategic approach in variable preprocessing.