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Logistic Regression Modeling

🎯 Purpose

This checklist provides a workflow for the modeling phase of a logistic regression project, from fitting the model to evaluating its performance and diagnosing potential issues.

🧭 Model Setup

  • [ ] EDA complete (see logistic EDA checklist)
  • [ ] Assumptions reviewed (logit linearity, collinearity)

⚙️ Model Fit

  • [ ] Used Logit() or LogisticRegression()
  • [ ] Interpreted coefficients and converted to odds ratios
  • [ ] Reviewed confidence intervals and p-values

🧪 Evaluation

  • [ ] Confusion matrix, precision, recall, F1
  • [ ] ROC curve + AUC
  • [ ] Classification report used for interpretation
  • [ ] Threshold Tuning: Reviewed precision-recall curve to select an optimal threshold if needed.

⚠️ Diagnostics

  • [ ] Reviewed Cook’s distance or leverage
  • [ ] Addressed misclassified cases
  • [ ] Considered model calibration if needed

🧠 Final Tip

"A logistic model's success isn't just its accuracy, but its ability to rank risk. Always check the AUC and consider if the default 0.5 threshold meets your business goal."