aeondave/scikit-learn
ai/scikit-learn/SKILL.md
scikit-learn model inspection workflow for loading persisted estimators, pipelines, and tree models. Use when you need to inspect `joblib` or pickle-based model artifacts, view parameters, feature names, importances, or pipeline structure, or run lightweight predictions for analysis.
$ install --global
skillsauthnpx skillsauth add aeondave/malskill scikit-learnInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- scikit-learn
- description:
- scikit-learn model inspection workflow for loading persisted estimators, pipelines, and tree models. Use when you need to inspect `joblib` or pickle-based model artifacts, view parameters, feature names, importances, or pipeline structure, or run lightweight predictions for analysis.
- compatibility:
- Linux, Windows, macOS; Python 3; scikit-learn and joblib installed
- author:
- AeonDave
- version:
- 1.0
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
joblibor 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
joblibis the common persistence format for sklearn models with large NumPy arrays.- Pipelines often carry more insight than the final estimator alone, so inspect
named_stepsearly. feature_names_in_andget_feature_names_out()are high-value clues when reconstructing model inputs.
Caveats
joblib.loadand 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.
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