scikit-learn

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

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

scikit-learn is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using scikit-learn should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/scikit-learn/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/scikit-learn/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/scikit-learn/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How scikit-learn Compares

Feature / Agent	scikit-learn	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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](https://scikit-learn.org/)