---

name: supervised-benchmarking description: "Use Lazy Predict LazyClassifier and LazyRegressor for low-code supervised classification and regression model benchmarking, result interpretation, categorical data handling, and fitted model access." disable-model-invocation: true metadata: disco-role: operating license: MIT

Supervised Benchmarking

Use this sub-skill when the task is to compare many sklearn-style classifiers or regressors quickly with Lazy Predict, interpret the ranked result table, recover failed models, or extract fitted pipelines for downstream prediction.

Start here

1. Decide whether the target is classification or regression.

2. Split data yourself with scikit-learn or an equivalent deterministic split.

3. Bound the run when speed matters: choose a small explicit model list, max_models, and/or timeout rather than using every estimator.

4. Run the bundled smoke helper if the environment or API contract is in doubt:

   python scripts/smoke_supervised.py --task both --max-models 1
   

Main APIs

Read references/api-reference.md for verified constructor signatures, fit() return shapes, selected-model syntax, provide_models(), predict(), and joblib persistence.

Typical bounded classification pattern:

from lazypredict.Supervised import LazyClassifier
from sklearn.linear_model import LogisticRegression

clf = LazyClassifier(
    classifiers=[LogisticRegression],
    verbose=0,
    ignore_warnings=True,
    predictions=True,
    max_models=1,
)
scores, predictions = clf.fit(X_train, X_test, y_train, y_test)

Typical bounded regression pattern:

from lazypredict.Supervised import LazyRegressor
from sklearn.linear_model import Ridge

reg = LazyRegressor(regressors=[Ridge], verbose=0, ignore_warnings=True)
scores, _ = reg.fit(X_train, X_test, y_train, y_test)

Data and results

Read references/data-and-results.md when the data has pandas categorical columns, boolean columns, custom metrics, cross-validation, prediction output, or model ranking columns that must be validated.

Key reminders:

• fit() returns a tuple (scores, predictions_df) for both classifiers and regressors. The predictions DataFrame is empty unless predictions=True.
• categorical_encoder accepts "onehot", "ordinal", "target", or "binary"; target and binary encoding require the optional category_encoders package.
• Inspect .errors after fitting when important models are absent from the result table.

Troubleshooting

Read references/troubleshooting.md for slow or failing estimators, invalid constructor arguments, empty/mismatched datasets, missing optional dependencies, hidden model errors, ROC-AUC surprises, GPU fallback, and model persistence failures.

Route elsewhere

• Use time-series-forecasting for LazyForecaster or forecast metrics.
• Use advanced-workflows for detailed tuning, search spaces, SHAP, and advanced explainability choices.
• Use cli-and-integrations for the CSV CLI, MLflow tracking setup, Dask/PySpark conversion, Spark classes, and package environment checks.