AI导哈

数据与研究

scikit-learn

k-dense-ai/scie…t-skills

Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill scikit-learn

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. 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.

# Install scikit-learn using uv
uv pip install "scikit-learn==1.9.1"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]==1.9.1" matplotlib pandas

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