aeondave/scikit-learn

scikit-learn

Use this when the artifact is an estimator, pipeline, or tree model rather than a deep-learning checkpoint.

When to use scikit-learn

Use scikit-learn when you need to:

• load persisted estimators or pipelines from joblib or pickle files
• inspect parameters, pipeline stages, and feature handling
• read feature importances or linear coefficients
• export or reason about decision-tree structure

Quick Start

import joblib

model = joblib.load("model.joblib")
print(type(model))
print(model.get_params().keys())

High-Value Workflows

Pipeline inspection

if hasattr(model, "named_steps"):
    print(model.named_steps)

if hasattr(model, "get_feature_names_out"):
    print(model.get_feature_names_out())

Feature importance or coefficients

if hasattr(model, "feature_importances_"):
    print(model.feature_importances_)

if hasattr(model, "coef_"):
    print(model.coef_)

Tree export helpers

from sklearn.tree import export_text

if hasattr(model, "tree_"):
    print(export_text(model))

Practical Notes

• joblib is the common persistence format for sklearn models with large NumPy arrays.
• Pipelines often carry more insight than the final estimator alone, so inspect named_steps early.
• feature_names_in_ and get_feature_names_out() are high-value clues when reconstructing model inputs.

Caveats

• joblib.load and pickle are unsafe for untrusted files.
• Cross-version loading is not guaranteed to be stable.
• Some estimators expose rich introspection, while others offer almost none beyond get_params().

Resources

No bundled scripts/, references/, or assets/. Use the official scikit-learn persistence and pipeline docs for version and API specifics.