---

name: train-model description: Train or retrain a credit scoring model. Use when the user asks to train, retrain, fine-tune, or calibrate a model, or when new training data is available.

Train model

Train or retrain an OpenCredit scoring model with full MLflow tracking and post-training validation.

Workflow

1. Validate prerequisites

• Confirm training data exists (check data/ or feature store)
• Confirm model config YAML exists in configs/models/
• Confirm MLflow is accessible (uv run mlflow ui or docker service)

2. Run training

uv run python -m opencredit.models.train \
  --config configs/models/<model_type>.yaml \
  --experiment-name <descriptive_name> \
  --tags market=<market> version=<semver>

3. Evaluate

After training completes, immediately run evaluation:

uv run python -m opencredit.models.evaluate \
  --model-id <mlflow_run_id> \
  --test-data data/test.parquet

Check these metrics meet thresholds:

• AUC-ROC ≥ 0.72
• Gini ≥ 0.44
• KS statistic ≥ 0.30
• Calibration: Brier score ≤ 0.20

4. Bias audit (MANDATORY before promotion)

uv run python -m opencredit.compliance.bias_audit \
  --model-id <mlflow_run_id> \
  --attributes gender age_group region

Fail criteria: disparate impact ratio outside 0.8-1.25 on ANY group.

5. Generate model card

uv run python -m opencredit.compliance.docs_generator \
  --model-id <mlflow_run_id> \
  --output docs/compliance/

6. Register in MLflow

Only if evaluation AND bias audit pass:

uv run python -m opencredit.models.register \
  --model-id <mlflow_run_id> \
  --stage production

Important

• NEVER skip the bias audit step, even for quick experiments.
• Log ALL hyperparameters — no magic numbers in training scripts.
• If training on new market data, create a new experiment in MLflow, don't reuse existing ones.
• Save the SHAP background dataset alongside the model artifact.