Scikit learn
Scikit-learn machine learning library. Use for classical ML.From its SKILL.md
npx -y skills add G1Joshi/Agent-Skills --skill scikit-learnAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Scikit-learn
Scikit-learn is the gold standard for "Classical ML" (Regression, SVM, Random Forest). v1.6 (2025) adds Array API support (running on GPUs via PyTorch/CuPy).
When to Use
- Tabular Data: Random Forests / Gradient Boosting.
- Preprocessing:
StandardScaler,LabelEncoder. - Small Data: When Deep Learning is overkill.
Core Concepts
Estimators
Everything implements .fit(X, y) and .predict(X).
Pipelines
Chaining preprocessing and modeling: Pipeline([('scaler', StandardScaler()), ('svc', SVC())]).
Array API
Passing PyTorch tensors directly to Scikit-learn without converting to NumPy (keeping data on GPU).
Best Practices (2025)
Do:
- Use Pipelines: Prevent data leakage during cross-validation.
- Use
HistGradientBoostingClassifier: It is much faster than standard extraction implementation (inspired by LightGBM).
Don't:
- Don't use for Images/Audio: Use PyTorch/DL for unstructured data.
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.