auto_modeling_audit
Evaluate if your data is ready for modeling: detects leakage, multicollinearity, instability, temporal issues, and class imbalance, then provides a verdict and mitigation steps.
Instructions
'Can I train a model on this without footguns?'
Chains leakage + multicollinearity + stability + temporal* + class imbalance
into a single ranked-blocker brief with a verdict (do_not_train_yet /
train_with_mitigations / ready_to_train).
Downsamples to `max_rows` via reservoir sampling when the source is
larger — the per-column correlations these checks run can time out on
7M+ row sources but converge to the same conclusions at ~500K rows.
Set `max_rows` higher (or to a number above the source size) for the
full pass. The sample is materialized once and surfaced in `sample_info`.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| max_rows | No | ||
| source_id | Yes | ||
| time_column | No | ||
| target_column | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||