Ballzatram

AI-guided workbenches, simulations, games, and odd tools

Governance

Model comparison and validation

Compare OLS, rolling windows, regularized models, and stress templates with transparent fit, stability, and interpretability tradeoffs.

Governance layer

Guided intake

Start with a decision, not a blank prompt

The workshop guide uses these intake points to ask sharper questions before producing structured output.

Goal

What decision should model comparison support?

Inputs

Which dataset, ticker, scenario, or assumption set should be trusted?

Constraint

What risk limit, horizon, or caveat should shape the output?

Output contract

Every useful answer becomes a card stack

Recommendation

What the workflow suggests and why it is not automatic advice.

Evidence

The metrics, chart movement, or model signal supporting the view.

Caveat

The assumption, missing data, or model risk that could change the result.

Next step

The most useful follow-up action before exporting or sharing.

Best validation

Ridge

Demo ranking after stability penalty

Drift flag

Medium

Coefficient movement above monitoring threshold

Models tracked

4

OLS, rolling OLS, ridge, and scenario templates

Professional review checklist

  • OKSeparate model-selection metrics from investment conclusions.
  • OKTrack when training windows or factor definitions change.
  • OKRequire human sign-off before operational use.

Empty state behavior

Run at least two model variants to populate the comparison matrix.

Correlation is not causation. Results are model-dependent and assume regime stability.
Placeholders only. No payment gate.