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name: lazypredict description: "Use Lazy Predict for low-code model benchmarking, supervised classification and regression sweeps, time-series forecasting comparisons, CLI CSV runs, optional tuning, explainability, MLflow, Spark, and dependency troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Lazy Predict Repo Skill

Use this skill when a task asks how to install, use, debug, or validate the lazypredict Python package. Lazy Predict runs many baseline machine-learning models with little code and returns ranked pandas DataFrames for comparing model families before deeper modeling work.

First checks

1. Confirm the package is installed in the user's Python environment:

   import lazypredict
   print(lazypredict.__version__)
   

2. For a broader local diagnostic, run the bundled checker:

   python scripts/check_lazypredict_env.py --json
   

3. Install only the extras needed for the requested workflow. Start with:

   pip install lazypredict
   

   Then add optional extras such as boost, timeseries, tune, explain, viz, spark, or mlflow only when the task needs them. Read references/install-and-troubleshooting.md before installing broad extras or GPU/Spark/foundation dependencies.

Route by task

• Read supervised-benchmarking for LazyClassifier and LazyRegressor: sklearn-style classification or regression sweeps, categorical columns, custom metrics, result DataFrames, model selection, progress callbacks, persistence, and fitted-pipeline access.
• Read time-series-forecasting for LazyForecaster: univariate or exogenous series, seasonal period detection, lag/rolling features, forecasting metrics, horizon strategies, ensembles, diagnostics, plotting, and optional statistical/deep/foundation forecasters.
• Read advanced-workflows for optional tuning, search spaces, permutation/SHAP explainability, FLAML/Optuna choices, and advanced smoke checks that should not be mixed into a first quick start.
• Read cli-and-integrations for the lazypredict CSV CLI, MLflow tracking, Dask/PySpark conversion, Spark MLlib classes, GPU/Intel optional acceleration checks, and integration failures.

Common decision points

• Prefer explicit model subsets, max_models, and timeout when the user needs a fast answer. Running every available estimator can be slow or noisy.
• Treat ignore_warnings=True as a benchmarking convenience, not as proof that every model succeeded. Inspect .errors when important models are missing.
• Use predictions=True only when downstream work needs per-model predictions; otherwise the second returned DataFrame is normally empty.
• GPU support is optional. use_gpu=True requests supported backends but many paths fall back to CPU unless CUDA-capable packages and hardware are present.
• The CSV CLI supports supervised classification/regression only. Use the Python API for time-series forecasting, tuning, explainability, or custom pipelines.

Provenance and refresh

Read references/repo-provenance.md before checking whether this skill is current for a different Lazy Predict checkout. If the package version, public entry points, or major workflow files changed, refresh the skill before relying on detailed API claims.