scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference docum
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 27 938
- Лиценз
- Не е посочен. Проверете в репозитория.
- Последна проверка на записа
- Няма посочена дата
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add K-Dense-AI/claude-scientific-skillsСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
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
# Commonly used with
uv pip install pandas numpy
Check your version:
import sklearn
print(sklearn.__version__)
When to Use This Skill
Use the scikit-learn skill when:
- Building classification or regression models
- Performing clustering or dimensionality reduction
- Preprocessing and transforming data for machine learning
- Evaluating model performance with cross-validation
- Tuning hyperparameters with grid or random search
- Creating ML pipelines for production workflows
- Comparing different algorithms for a task
- Working with both structured (tabular) and text data
- Need interpretable, classical machine learning approaches