Even though logistic regression does not require the adherence to any assumptions about the distribution of predictor variables, several problems may occur if too few cases relative to the number of predictor variables exist in the data.

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Logistic regression does not assume a linear relationship between the dependent and independent variables, nor does it require the independent variables to be normally distributed. However, having too few cases (observations) relative to the number of predictor variables can lead to overfitting, where the model performs well on the training data but poorly on new, unseen data. This is because the model may capture noise rather than the underlying pattern, making it less generalizable.

Even Though Logistic Regression Does Not Require the Adherence to Any