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Scikit Learn Skill by @K-Dense-AI

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github stars★ 55,163updatedOct 2026repo@K-Dense-AI

activity (commits, last 6 months)

6mo ago2

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$git clone https://github.com/K-Dense-AI/scientific-agent-skills

The skill is the skills/scikit-learn folder inside that repository. Copy it into the folder your agent loads skills from; check your agent's documentation for where that is.

about Scikit Learn Skill by @K-Dense-AI

Supports machine learning in Python with scikit-learn.

our take

Machine learning in Python with scikit-learn: supervised learning such as classification and regression, unsupervised methods like clustering and dimensionality reduction, and model evaluation, with a quick start and examples.

when to use it

You build a classifier or regression model
You need clustering or dimensionality reduction
You want evaluation done properly, with train and test splits

before you install

Models are only as good as the data, so check leakage and class balance
Install the library first, since the skill assumes it is available

what's inside

The original SKILL.md is organised into these sections:

Overview
Installation
When to Use This Skill
Quick Start
Classification Example
Complete Pipeline with Mixed Data
Core Capabilities
Example Scripts
Classification Pipeline
Clustering Analysis
Reference Documentation
Quick Reference

source and license

Written and maintained by @K-Dense-AI. It is published under the MIT license, which allows reuse with attribution. SkillDirectory does not claim authorship: this page adds our own review and links back to the original, and the skill's text belongs to its author.

View the full SKILL.md on GitHub ↗

Read the opening of the original SKILL.md

# Scikit-learn

## Overview

This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.

## Installation

Targets **scikit-learn 1.9.1**, verified with Python 3.13. The release requires Python 3.11+; use its published wheels for your interpreter/platform. See the [1.9 release notes](https://scikit-learn.org/stable/whats_new/v1.9.html). The bundled scripts and regression tests are executable examples. Reference snippets using caller-provided `X`, `y`, columns, or placeholders are illustrative adaptations, not complete standalone programs.

Install the PyPI package **`scikit-learn`** (not the deprecated `sklearn` package on PyPI). Import in code as `sklearn`.

```bash
# Install scikit-learn using uv
…

Excerpt only, quoted from the author's repository. Open the repository for the complete file.

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