Step 1: Restate the problem
You’ve tried logistic regression, but the results are unsatisfactory—maybe accuracy is low, features aren’t capturing the right signal, or the linear assumption is too restrictive. The next step is to systematically explore ways to improve performance, starting with logistic regression itself before moving to alternative models.
Step 2: Improve feature engineering
Logistic regression assumes a linear relationship between features and the log-odds of the outcome. If the signal is nonlinear or interactions matter, the model struggles. Ways to improve:
- Add interaction terms (e.g., \(x_1 \times x_2\)).
- Add nonlinear transforms (e.g., \(\log(x)\), \(x^2\)).
- Normalize/standardize features to help optimization.
- Encode categorical variables properly (one-hot, embeddings for high-cardinality).
- Reduce noise with feature selection or dimensionality reduction (PCA, autoencoders).
Step 3: Adjust regularization and hyperparameters
Logistic regression performance is sensitive to regularization:
- L1 (Lasso): encourages sparsity, can act as feature selection.
- L2 (Ridge): shrinks coefficients, reduces variance.
- Elastic Net: balances both.
Tuning the regularization strength CC can significantly improve generalization.
Step 4: Address class imbalance
If the dataset is imbalanced, logistic regression may be biased toward the majority class. Remedies:
- Use class weights to penalize mistakes on the minority class.
- Resampling: oversample minority (SMOTE) or undersample majority.
- Evaluate with precision/recall, ROC-AUC, or PR-AUC rather than accuracy.
Step 5: Try more expressive models
If logistic regression still underperforms, consider models that capture nonlinearity and complex interactions:
- Decision Trees: simple nonlinear splits.
- Random Forests: ensembles of trees for variance reduction.
- Gradient Boosting (XGBoost, LightGBM, CatBoost): powerful, handles nonlinearity well, widely used in tabular data.
- Support Vector Machines (SVMs) with kernels: useful when classes are separable in higher dimensions.
- Neural Networks: if you have large amounts of data or complex patterns.
Step 6: Validate and compare models
Use proper validation (k-fold CV or time-based splits) to compare logistic regression with these alternatives. Always balance performance with interpretability and computational cost—logistic regression is highly interpretable, while boosted trees may be less so but much more powerful.
Step 7: Put it together
Start by improving logistic regression with better features, transformations, and regularization. If the linear framework is still too restrictive, move to tree-based methods or gradient boosting, which are often state-of-the-art for tabular data. Always ground your choice in the trade-off between performance, interpretability, and business needs.
