pipelines_and_composition
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.
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Install (Claude Code)
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.mdPaste 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
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