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name: advanced-workflows description: "Use Lazy Predict optional advanced workflows for tuning, search spaces, explainability, feature importance, SHAP, Optuna, FLAML, visualization pointers, and safe advanced dependency checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Advanced Workflows

Use this sub-skill when the task goes beyond a first Lazy Predict leaderboard: hyperparameter tuning, search-space inspection, permutation or SHAP explainability, FLAML/Optuna decisions, optional visualization dependencies, or advanced smoke checks.

Start here

1. Run a small core benchmark first with the supervised or time-series sub-skill. Advanced APIs usually need fitted models or score tables.

2. Check optional dependencies before promising SHAP, Optuna, FLAML, InterpretML, or plotting.

3. Keep trials, top-k counts, and timeouts bounded. Advanced workflows can be much more expensive than the initial Lazy Predict sweep.

4. Run the bundled advanced smoke helper for a quick environment check:

   python scripts/smoke_advanced.py --json
   

What to read

• references/advanced-workflows.md covers supervised and time-series tuning, search-space registries, permutation/SHAP explainability, and dependency choices.
• references/troubleshooting.md covers missing optional packages, invalid tuning parameters, slow or empty tuning runs, SHAP compatibility, and unfitted model errors.

Common routes

• For LazyClassifier(..., tune=True) or LazyRegressor(..., tune=True), read the supervised tuning section and then return to supervised-benchmarking for the base fit contract.
• For LazyForecaster(..., tune=True), read the time-series tuning section and then return to time-series-forecasting for forecasting data and metrics.
• For feature importance after supervised fitting, prefer permutation importance as the base dependency path; use SHAP only when the shap extra is installed and the fitted model family is compatible.
• For MLflow, Dask/PySpark, Spark, CLI, or GPU environment checks, use cli-and-integrations.

Do not use this sub-skill for

• A first quick model comparison without tuning or explanation.
• Repository maintenance, release automation, or documentation publishing.
• Full production AutoML orchestration outside Lazy Predict's exposed APIs.