# pipelines_and_composition

> Pipelines chain multiple processing steps into a single estimator, preventing data leakage and simplifying code. They enable reproducible workflows and sea

- Source: https://doutorai.pt/en/components/skills__pipelines-and-composition
- Type: Skills
- Category: Scientific
- Downloads: 0
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

An open-source community component, curated and adapted by Doutor AI.

## What it does and how to use it

A **skill** that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

1. Install the skill (command above).
2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
3. No commands to memorize: you talk in plain language and the skill does the rest.

## Install (Claude Code)

```bash
mkdir -p .claude/skills/pipelines-and-composition && curl -fsSL https://doutorai.pt/data/lib/skills__pipelines-and-composition.md -o .claude/skills/pipelines-and-composition/SKILL.md
```

Paste this command into the terminal, in your project folder, and the component installs automatically.

## Content

````
# Pipelines and Composite Estimators Reference

## Overview

Pipelines chain multiple processing steps into a single estimator, preventing data leakage and simplifying code. They enable reproducible workflows and seamless integration with cross-validation and hyperparameter tuning.

## Pipeline Basics

### Creating a Pipeline

**Pipeline (`sklearn.pipeline.Pipeline`)**
- Chains transformers with a final estimator
- All intermediate steps must have fit_transform()
- Final step can be any estimator (transformer, classifier, regressor, clusterer)
- Example:
```python
from sklearn.pipeline import 

- 🔒 Full content on the Pro plan (€7.99/month) -
````

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/components/skills__pipelines-and-composition