Classifier Model
🎯 Purpose
This card provides a quick-reference tool to help analysts choose the right classification model based on problem characteristics, business requirements, and data constraints. Use it during exploration, modeling, or stakeholder alignment.
⚙️ Step 1: Problem Characteristics
| Question |
If Yes... |
Then Consider... |
| Is the target binary? |
✅ |
Logistic Regression, NB, Tree |
| Is the target multiclass (3+ classes)? |
✅ |
Random Forest, Gradient Boosting |
| Is class imbalance present? |
✅ |
Weighted Logistic, XGB, PR Curve |
| Do you need model probabilities? |
✅ |
Calibrated Logistic, NB, XGB |
| Is interpretability important? |
✅ |
Logistic Regression, Decision Tree |
| Is accuracy more important than speed? |
✅ |
Random Forest, XGBoost |
| Is the dataset large and noisy? |
✅ |
Gradient Boosting, RF |
| Is data mostly text/categorical? |
✅ |
Naive Bayes, Tree-based models |
🧰 Step 2: Model Preference Guide
| Preference |
Recommended Models |
| 🔎 Interpretability |
Logistic Regression, Decision Tree |
| ⚡️ Speed / Simplicity |
Naive Bayes, Logistic, KNN |
| 🧠 Nonlinear Flexibility |
Random Forest, XGBoost, LightGBM |
| 🧪 Probabilistic Output |
Logistic (calibrated), Naive Bayes |
| 📈 Feature Impact |
Tree models with SHAP or permutation |
| ⚖️ Imbalance Handling |
Weighted Logistic, XGB, SMOTE+Tree |
| 🧬 Mixed Feature Types |
Tree-based models, Ensemble Pipelines |
📊 Step 3: Diagnostic Strategy by Model
| Model Type |
Suggested Diagnostics |
| Logistic Regression |
ROC/AUC, Confusion Matrix, Calibration Curve |
| Naive Bayes |
Confusion Matrix, PR Curve |
| Random Forest |
SHAP, Feature Importance, ROC |
| XGBoost |
SHAP, Log-Loss, Calibration Curve |
| SVM |
ROC, Confusion Matrix |
| KNN |
Confusion Matrix, PR Curve |
| Neural Networks |
Confidence Histogram, PR Curve, Calibration |
🧠 Final Reminders
- 🎯 Start simple, baseline with Logistic or Naive Bayes.
- ⚖️ Tune thresholds and test with multiple metrics.
- 📉 Visuals improve communication — always include CM, ROC/PR.
- 🔍 Use SHAP for any ensemble or "black-box" model in production.
💡 Tip
"When in doubt between multiple classifiers, run a quick cross-validation benchmark on a small feature set. This gives you a realistic comparison of accuracy, training time, and interpretability before committing to a model family."why