Troubleshooting Logistic Regression: Common Issues &Clear Solutions

Logistic regression is a powerful and interpretable tool for classification, but sometimes models don’t behave as expected. This guide lists the most common issues you might encounter — and how to fix them.

1. Model Won’t Converge

Cause: Features may be on very different scales, or the learning process is too slow.

Solution: Standardize your features using techniques like z-score normalization or StandardScaler in scikit-learn.

2. Poor Accuracy

Cause: Data may be imbalanced, with far more examples of one class than the other.

Solution: Use methods like SMOTE (Synthetic Minority Oversampling Technique) or set class_weight='balanced' when fitting your model.

3. Multicollinearity

Cause: Highly correlated features make coefficient estimates unstable.

Solution: Check correlations and variance inflation factors (VIF). Remove or combine correlated predictors.

4. Overfitting

Cause: Model is too complex or over-trained, capturing noise instead of signal.

Solution: Use regularization (L1 or L2 penalty) or collect more data. Cross-validation can help verify model performance.

5. Underfitting

Cause: Model is too simple to capture the patterns in data.

Solution: Add relevant features, use interaction terms, or allow more training iterations.

6. Misinterpreting Coefficients

Cause: Forgetting that logistic regression coefficients represent log-odds, not direct changes in probability.

Solution: Convert coefficients to odds ratios using np.exp(coef) for better interpretability.

7. Choosing the Wrong Threshold

Cause: Default 0.5 cutoff may not balance precision and recall for your problem.

Solution: Plot a precision-recall curve or ROC curve to find the optimal threshold.

Quick Reference Table

By systematically diagnosing these issues, you can greatly improve the predictive performance and interpretability of your logistic regression model.