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The inclusion of replicated features does not necessarily enhance the accuracy of Logistic Regression. In fact, it can lead to overfitting, where the model becomes too complex and starts to fit to noise in the data rather than the underlying pattern.

Replicated features are features that are highly correlated with other features in the dataset. Including these features in the model can cause the coefficients of the included features to become unstable, leading to unreliable predictions.

Therefore, it is usually recommended to remove replicated features before fitting a Logistic Regression model to improve the model's accuracy and stability.