Despite its advantages, logistic regression has some limitations:
Linearity assumption: It assumes a linear relationship between the dependent and independent variables, which may not always hold true. Overfitting: With too many features, the model can overfit the training data, reducing its predictive power on new data. Binary outcome: Logistic regression is primarily designed for binary outcomes. For multi-class problems, extensions like multinomial logistic regression are needed.