Retrain ML model on real feedback (replace synthetic bootstrap)

[ringo380]

Context

The currently-ACTIVE model (query_grader, version 20260521_021306) was trained as a bootstrap on synthetic/seed TrainingData to give the issue-#5 monitoring pipeline an ACTIVE target. Its metrics reflect that:

• Training accuracy: 0.804
• Validation accuracy: 0.712 (just over the 0.7 auto-deploy threshold)
• Test accuracy: 0.095 — effectively no real predictive power.

It satisfies "an ACTIVE model exists so monitoring evaluates," but it is not a quality predictor. The low-confidence / low-user-agreement alerts it generates reflect exactly this.

Goal

Replace the synthetic bootstrap with a model trained on accumulated real user feedback once enough has been collected.

Tasks

• Define a minimum real-feedback sample threshold before retraining (current ML_MIN_TRAINING_SAMPLES=50 counts synthetic seed rows — may want a separate "validated real feedback" gate).
• Confirm process_ml_feedback → TrainingData flow is converting real QueryFeedback into training samples.
• Establish a retrain cadence (the ML_AUTO_RETRAIN / beat machinery exists) and a quality gate higher than the bootstrap's 0.7 val / 0.095 test.
• Re-evaluate model_type: bootstrap is QUERY_GRADER; the grading path expects HYBRID_SCORER (per CLAUDE.md) once HybridQueryGrader is wired in.

Notes

• Training runs in-container: railway ssh -s querygrade-worker "python manage.py train_ml_model …" (local railway run can't reach postgres.railway.internal).
• Related: model-artifact durability (Move ML model artifacts to object storage (model registry) #91) becomes relevant once predictions actually load the model.