scikit-learn

1# Scikit-learn
2 
3## Overview
4 
5This 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.
6 
7## Installation
8 
9```bash
10# Install scikit-learn using uv
11uv pip install scikit-learn
12 
13# Optional: Install visualization dependencies
14uv pip install matplotlib seaborn
15 
16# Commonly used with
17uv pip install pandas numpy
18```
19 
20## When to Use This Skill
21 
22Use the scikit-learn skill when:
23 
24- Building classification or regression models
25- Performing clustering or dimensionality reduction
26- Preprocessing and transforming data for machine learning
27- Evaluating model performance with cross-validation
28- Tuning hyperparameters with grid or random search
29- Creating ML pipelines for production workflows
30- Comparing different algorithms for a task
31- Working with both structured (tabular) and text data
32- Need interpretable, classical machine learning approaches
33 
34## Quick Start
35 
36### Classification Example
37 
38```python
39from sklearn.model_selection import train_test_split
40from sklearn.preprocessing import StandardScaler
41from sklearn.ensemble import RandomForestClassifier
42from sklearn.metrics import classification_report
43 
44# Split data
45X_train, X_test, y_train, y_test = train_test_split(
46    X, y, test_size=0.2, stratify=y, random_state=42
47)
48 
49# Preprocess
50…