Model Comparison Workflow

This guide shows how to compare candidate models using TrustLens and select a deployment candidate with explicit risk trade-offs.

When to Use This

Use this workflow when two or more models have similar accuracy and you need a reliability-first decision.

Workflow

  1. Train candidate models on the same train split.

  2. Run analyze() on each model using the same validation data.

  3. Compare trust reports with compare().

  4. Review blocker status, penalties, and dimension-level differences.

  5. Select the candidate with highest safe trust profile, not only highest score.

Example

from trustlens import analyze, compare

report_rf = analyze(model_rf, X_val, y_val, y_prob=model_rf.predict_proba(X_val))
report_lr = analyze(model_lr, X_val, y_val, y_prob=model_lr.predict_proba(X_val))

compare([report_rf, report_lr])

Decision Checklist

  • Are any candidates blocked from deployment?

  • Which model has lower failure penalty burden?

  • Are fairness penalties acceptable for your domain?

  • Is calibration quality sufficient for confidence-based decisions?