If you remove highly correlated variables from a binary classifier model, how does this affect its accuracy?

Removing highly correlated variables from a binary classifier model typically does not significantly impact the model's accuracy. Highly correlated variables often provide redundant information, meaning they do not contribute unique insights into the predictive power of the model. By eliminating these variables, you simplify the model without losing essential information, which can even help prevent issues such as multicollinearity.

Multicollinearity can lead to instability in the model's coefficient estimates, making it harder to interpret the impact of individual predictors on the outcome variable. Thus, cleaning up these redundant variables helps focus the model on the most informative features while maintaining or potentially improving overall performance.

The situation could vary based on specific data characteristics, but generally, removing highly correlated features tends not to have a significant negative impact on accuracy and often improves model interpretability.