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Feature Importance Analyzer

Listed
jeremylongshore·vlatest·other·MIT

This tool quickly identifies key features driving model predictions, boosting understanding and improving data-driven decisions.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jeremylongshore/tons-of-skills-marketplace --skill feature-importance-analyzer -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jeremylongshore

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

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Not scanned. none of the 396 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as jeremylongshore/claude-code-plugins-plus-skills.
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About this skill

What it does

This skill analyzes machine learning models and provides insights into feature importance. It identifies which input features have the most significant impact on model predictions, allowing users to understand how different variables contribute to outcomes. The analysis can be presented in various formats, aiding in model interpretability and potential feature engineering efforts.

When to use it

  • Model Debugging: Identify unexpected or problematic feature contributions when a model's performance is unsatisfactory.
  • Feature Selection: Determine which features are most valuable for building simpler, more efficient models.
  • Explainable AI (XAI): Provide stakeholders with clear explanations of how a model arrives at its decisions.
  • Data Understanding: Gain insights into the underlying relationships between input variables and model outputs.

Key capabilities

  • Feature importance ranking
  • Model interpretation
  • Variable contribution analysis
  • Support for various machine learning models (specific types not listed)

Example prompts

  • "Analyze the feature importance of my customer churn prediction model."
  • "Show me which features are most influential in determining loan approval probability."
  • "Explain why this image classification model is focusing on the background instead of the object."

Tips & gotchas

This skill requires a trained machine learning model as input. The accuracy and usefulness of the feature importance analysis depend heavily on the quality and characteristics of the underlying model.