In a logistic regression model, what is the effect of high skewness in predictor variables on model performance?

High skewness in predictor variables can significantly impact the performance of a logistic regression model. When predictor variables are highly skewed, their distributions are asymmetrical, which can lead to difficulties in estimating the relationships between predictors and the response variable accurately.

Transforming skewed predictors, such as using logarithmic or square root transformations, can help normalize the distribution of these variables. By correcting the skewness, the relationship between the predictors and the outcome can become more linear, which is an essential assumption of logistic regression. This normalizing process often leads to improved model fit and, consequently, better predictive performance.

In contrast, leaving skewed variables in their original form can introduce biases in coefficient estimates. This can lead to reduced prediction accuracy or strange inferences about relationships that do not align with the underlying data patterns. Therefore, transforming skewed predictor variables not only mitigates these issues but also makes the logistic regression model more robust and capable of generalizing well to unseen data.