home / skills / jeremylongshore / claude-code-plugins-plus-skills / engineering-features-for-machine-learning
/plugins/ai-ml/feature-engineering-toolkit/skills/engineering-features-for-machine-learning
This skill engineers features for machine learning by creating, selecting, and transforming data to boost model performance.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill engineering-features-for-machine-learningReview the files below or copy the command above to add this skill to your agents.
---
name: engineering-features-for-machine-learning
description: |
Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <[email protected]>
license: MIT
---
# Feature Engineering Toolkit
This skill provides automated assistance for feature engineering toolkit tasks.
## Overview
This skill provides automated assistance for feature engineering toolkit tasks.
This skill enables Claude to leverage the feature-engineering-toolkit plugin to enhance machine learning models. It automates the process of creating new features, selecting the most relevant ones, and transforming existing features to better suit the model's needs. Use this skill to improve the accuracy, efficiency, and interpretability of machine learning models.
## How It Works
1. **Analyzing Requirements**: Claude analyzes the user's request and identifies the specific feature engineering task required.
2. **Generating Code**: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.
3. **Executing Task**: The generated code is executed, creating, selecting, or transforming features as requested.
4. **Providing Insights**: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of newly created features or the impact of transformations on model performance.
## When to Use This Skill
This skill activates when you need to:
- Create new features from existing data to improve model accuracy.
- Select the most relevant features from a dataset to reduce model complexity and improve efficiency.
- Transform features to better suit the assumptions of a machine learning model (e.g., scaling, normalization, encoding).
## Examples
### Example 1: Improving Model Accuracy
User request: "Create new features from the existing 'age' and 'income' columns to improve the accuracy of a customer churn prediction model."
The skill will:
1. Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age / income).
2. Execute the code and evaluate the impact of the new features on model performance.
### Example 2: Reducing Model Complexity
User request: "Select the top 10 most important features from the dataset to reduce the complexity of a fraud detection model."
The skill will:
1. Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).
2. Execute the code and select the top 10 features based on their importance scores.
## Best Practices
- **Data Validation**: Always validate the input data to ensure it is clean and consistent before performing feature engineering.
- **Feature Scaling**: Scale numerical features to prevent features with larger ranges from dominating the model.
- **Encoding Categorical Features**: Encode categorical features appropriately (e.g., one-hot encoding, label encoding) to make them suitable for machine learning models.
## Integration
This skill integrates with the feature-engineering-toolkit plugin, providing a seamless way to create, select, and transform features for machine learning models. It can be used in conjunction with other Claude Code skills to build complete machine learning pipelines.
## Prerequisites
- Appropriate file access permissions
- Required dependencies installed
## Instructions
1. Invoke this skill when the trigger conditions are met
2. Provide necessary context and parameters
3. Review the generated output
4. Apply modifications as needed
## Output
The skill produces structured output relevant to the task.
## Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
## Resources
- Project documentation
- Related skills and commands
This skill automates feature engineering workflows to create, select, and transform features that improve machine learning model performance. It generates and runs Python code that handles scaling, encoding, and feature importance analysis. Use it to accelerate iterative feature design and to produce reproducible, validated feature sets for modeling.
I analyze the user's request to determine the required engineering steps, then generate Python code using the feature-engineering-toolkit patterns: data validation, transformations, and selection. The code executes feature creation (interactions, aggregations), transformations (scaling, encoding, imputing), and importance analysis (model-based or statistical). Finally, I return the results, metric comparisons, and easy-to-apply code snippets or saved artifacts.
What inputs do I need to provide?
Provide the dataset (or a sample), target column, and any constraints or preferred transforms; specify desired output format if needed.
How does the skill prevent overfitting from created features?
It validates features using cross-validation, reports performance delta, and recommends conservative selection thresholds and regularization.