scikit-learn

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Translated

Scikit-learn machine learning library. Use for classical ML.

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Sourceg1joshi/agent-skills

Added on2026-02-11

NPX Install

npx skill4agent add g1joshi/agent-skills scikit-learn

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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

Scikit-learn Documentation