---

name: tabular-workflows description: "Use Igel's classic tabular CLI and Python workflows for init, fit, evaluate, predict, experiment, export, model and metric catalogs, preprocessing, cross-validation, hyperparameter search, clustering, and multi-output classic ML." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tabular Workflows

Use this sub-skill when the task is about Igel's classic tabular/classic-ML path: creating an igel.yaml or JSON config, fitting a model, evaluating a saved model, generating predictions, running the CLI experiment shortcut, exporting a fitted sklearn model to ONNX, listing supported models/metrics, or using Igel(**kwargs) from Python.

Start here:

• references/workflows.md for copyable init, fit, evaluate, predict, experiment, export, models, metrics, version, info, and Python API recipes.
• references/configuration.md for YAML/JSON config syntax, target-list rules, model arguments, preprocessing, splits, CV, multi-output, clustering, and hyperparameter search.
• references/data-formats.md for CSV/TXT/XLSX/JSON/HTML reading behavior, read_data_options, feature/target column expectations, and preprocessing data caveats.
• references/model-catalog.md for exact classic model names, model-type routing, CV-estimator notes, and metrics surfaced by igel metrics.
• references/api-reference.md for the verified Igel(**cli_args) call surface, core helper signatures, output artifacts, and programmatic caveats.
• references/troubleshooting.md for legacy dependency/import failures, malformed configs, missing targets, unsupported algorithms, artifact-path issues, and ONNX export shape limitations.
• scripts/run_tabular_cycle.py for a safe dry-run-first wrapper around fit/evaluate/predict/export payloads and a tiny optional fit/export demo.

Route boundaries:

1. Stay here for classic igel CLI commands: init, fit, evaluate, predict, experiment, export, models, metrics, version, and info.
2. Route FastAPI serving, /predict HTTP payloads, Python REST clients, Docker, and GUI questions to deployment.
3. Route AutoKeras, image/text/structured Auto-ML, IgelCNN, and missing auto-train questions to auto-ml.
4. Route uncertain repo-wide questions back to the root router instead of guessing between classic tabular, deployment, and Auto-ML.
5. Do not tell future agents to open the original examples or docs; the reusable syntax, examples, and caveats needed for this route are distilled into the bundled references above.

Operational defaults:

• Prefer .yaml or .json config files. Avoid .yml with this version unless a newer installed package proves the extension dispatch changed.
• Run fit/evaluate/predict from a stable working directory so the model_results/ artifacts remain together.
• Treat hyperparameter search, cross-validation, and ONNX export as explicit user-approved steps because they can add runtime cost or dependency/shape constraints.