An automated AI agent skill that identifies and selects the most relevant features in a dataset to streamline model training and improve interpretability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ml-feature-selector
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ml-feature-selector using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Machine Learning Feature Selector?
The Machine Learning Feature Selector is a specialized tool designed to handle the critical task of dimensionality reduction within your AI workflows. As a core component of the Openclaw Skills ecosystem, it allows developers to programmatically refine their datasets using industry-standard statistical methods. By filtering out noise and redundant data, this skill ensures that your machine learning models are both efficient and accurate.
Integrating this skill into your agentic pipelines helps maintain high-performance standards by focusing computational resources only on the variables that matter most. Whether you are building complex predictive models or simple classifiers, leveraging Openclaw Skills like this one simplifies the preprocessing stage of the machine learning lifecycle.
Machine Learning Feature Selector Use Cases
- Automated feature engineering for large-scale tabular datasets.
- Reducing model training time and computational costs by eliminating irrelevant inputs.
- Improving model interpretability by isolating the most predictive variables.
- Cleaning datasets during the preprocessing phase of an Openclaw Skills automated pipeline.
How Machine Learning Feature Selector Works
- The skill accepts a dataset URL and a target number of features as input parameters.
- It executes Univariate Selection to rank features based on individual statistical significance.
- Recursive Feature Elimination (RFE) is employed to iteratively rank and prune features based on model performance.
- Correlation Analysis identifies and removes highly correlated features to prevent redundancy.
- The skill returns a JSON object containing the list of the most relevant features for the next step in your workflow.
Machine Learning Feature Selector Setup
To integrate this capability into your project, ensure your environment is configured to interact with Openclaw Skills.
# Install the skill via the Openclaw CLI
openclaw install ml-feature-selector
# Set up your SkillPay.me credentials for micropayments
export SKILLPAY_API_KEY="your_api_key_here"
Once installed, you can trigger the skill by providing a valid CSV URL and the desired feature count. Each execution will automatically handle the 0.001 USDT payment via the integrated SkillPay.me service.
Machine Learning Feature Selector Data Schema & Taxonomy
| Property | Type | Description |
|---|---|---|
dataset |
string | The source URL of the CSV dataset to be analyzed. |
num_features |
integer | The specific number of top features to return. |
selected_features |
array | A list of strings identifying the optimized feature names. |
success |
boolean | A status flag indicating the success of the selection process. |
message |
string | A summary of the feature selection results. |
Machine Learning Feature Selector Advanced Features
- Multi-stage selection process combining statistical tests and recursive elimination.
- Intelligent correlation filtering to ensure feature independence.
- Seamless payment integration using SkillPay.me for low-cost, high-frequency API calls.
- Optimized for use within multi-agent Openclaw Skills architectures for end-to-end data science automation.
SKILL.md
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