# Data Science Scikit Learn Machine Learning

> Use when working with scikit-learn — machine learning, classification, regression, clustering

- Skill: `liaosw97/data-science-scikit-learn-machine-learning` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add liaosw97/data-science-scikit-learn-machine-learning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/liaosw97/data-science-scikit-learn-machine-learning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: liaosw97 (https://skillmd.com/u/liaosw97)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/liaosw97/data-science-scikit-learn-machine-learning

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# Scikit-learn 机器学习指南

## 数据预处理

### 管道操作(Pipeline)
```python
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(n_estimators=100))
])
```

## 模型训练

### 交叉验证最佳实践
```python
from sklearn.model_selection import cross_val_score

scores = cross_val_score(
    pipeline, X, y, 
    cv=5,  # 5折交叉验证
    scoring='accuracy'
)
print(f"平均准确率: {scores.mean():.2f} ± {scores.std():.2f}")
```