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home / skills / jeremylongshore / claude-code-plugins-plus-skills / sklearn-pipeline-builder

sklearn-pipeline-builder skill

/skills/07-ml-training/sklearn-pipeline-builder

This skill helps you build production-ready sklearn pipelines with step-by-step guidance and best-practice configurations for ML training.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill sklearn-pipeline-builder

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "sklearn-pipeline-builder"
description: |
  Build sklearn pipeline builder operations. Auto-activating skill for ML Training.
  Triggers on: sklearn pipeline builder, sklearn pipeline builder
  Part of the ML Training skill category. Use when working with sklearn pipeline builder functionality. Trigger with phrases like "sklearn pipeline builder", "sklearn builder", "sklearn".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Sklearn Pipeline Builder

## Overview

This skill provides automated assistance for sklearn pipeline builder tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "sklearn pipeline builder" in your request
- Ask about sklearn pipeline builder patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for sklearn pipeline builder
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with sklearn pipeline builder"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates construction and validation of scikit-learn pipelines for supervised and unsupervised workflows. It generates production-ready Python code, suggests sensible defaults, and guides you through data preparation, feature engineering, model selection, and hyperparameter tuning. Use it to speed development and enforce reproducible ML training patterns.

How this skill works

The skill inspects your dataset schema, requested model types, and training constraints to assemble a Pipeline or ColumnTransformer with preprocessing, feature selectors, and an estimator. It outputs runnable sklearn code, configuration snippets for cross-validation and GridSearchCV/RandomizedSearchCV, and basic experiment tracking hooks. It also validates common issues like incompatible shapes, missing transformers, and estimator parameter mismatches.

When to use it

  • Building end-to-end sklearn pipelines for model training and evaluation
  • Standardizing preprocessing across experiments (scaling, imputation, encoding)
  • Preparing production-ready pipeline code for deployment or CI pipelines
  • Setting up hyperparameter search and cross-validation for robust model selection
  • Teaching or documenting pipeline patterns and reproducible workflows

Best practices

  • Define clear column groups (numeric, categorical, text) and use ColumnTransformer to isolate preprocessing
  • Keep transformers stateless where possible and persist fitted pipelines for production use
  • Use Pipeline with a final estimator to ensure parameter names map correctly for hyperparameter search
  • Validate pipeline outputs on a holdout set and include simple experiment metadata (seed, CV folds, scoring)
  • Automate dependency and environment pinning so generated code runs reproducibly

Example use cases

  • Create a pipeline that imputes missing values, scales numeric features, one-hot encodes categoricals, and trains a RandomForestClassifier
  • Generate code for a GridSearchCV over preprocessing options and model hyperparameters with nested cross-validation
  • Convert a notebook workflow into a single serialized sklearn Pipeline for deployment
  • Produce minimal reproducible examples for code reviews or training materials

FAQ

Can this generate pipelines for custom transformers?

Yes. Provide the custom transformer class or a clear API description and the skill will insert it into the pipeline scaffold and show how to fit/serialize it.

Does it handle feature unions and complex column logic?

Yes. The skill can produce ColumnTransformer and FeatureUnion compositions and recommend grouping strategies for multi-modal datasets.