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home / skills / jeremylongshore / claude-code-plugins-plus-skills / feature-importance-analyzer

feature-importance-analyzer skill

/skills/07-ml-training/feature-importance-analyzer

This skill helps you automate feature importance analysis in ML training by providing step-by-step guidance, code, and validations.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill feature-importance-analyzer

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: "feature-importance-analyzer"
description: |
  Analyze feature importance analyzer operations. Auto-activating skill for ML Training.
  Triggers on: feature importance analyzer, feature importance analyzer
  Part of the ML Training skill category. Use when analyzing or auditing feature importance analyzer. Trigger with phrases like "feature importance analyzer", "feature analyzer", "analyze feature importance r".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Feature Importance Analyzer

## Overview

This skill provides automated assistance for feature importance analyzer tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "feature importance analyzer" in your request
- Ask about feature importance analyzer 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 feature importance analyzer
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 feature importance analyzer"
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 analysis and guidance for feature importance analyzer tasks within ML training workflows. It helps you inspect, validate, and act on feature importance results to improve model performance and interpretability. The skill is auto-activating for requests mentioning feature importance analyzer and is focused on practical, production-ready outcomes.

How this skill works

The skill inspects feature importance outputs from models and tools (e.g., SHAP, permutation importance, tree-based importances) and validates them against common standards. It provides step-by-step remediation, generates reproducible code and configuration snippets, and recommends follow-up experiments such as feature selection, transformation, or retraining. It also flags common pitfalls like data leakage, correlated features, and unstable importance scores across folds.

When to use it

  • You need to interpret model feature importances for audit or reporting
  • Validating feature importance pipelines during ML training or CI
  • Investigating model behavior after data drift or feature changes
  • Designing feature selection or dimensionality reduction experiments
  • Creating reproducible reports or code for model explainability

Best practices

  • Compare multiple importance methods (SHAP, permutation, model-specific) before acting
  • Run importance analysis across cross-validation folds to assess stability
  • Check for correlated features and perform conditional analyses where appropriate
  • Avoid removing features solely on single-run importance; validate downstream impact
  • Log importance results with experiment tracking and include seed and preprocessing details

Example use cases

  • Generate a Python notebook snippet that computes SHAP values and plots top features for a trained model
  • Audit a feature importance report to identify potential data leakage or label leakage
  • Produce a reproducible pipeline to compute permutation importance as part of CI for model training
  • Recommend a feature selection plan based on unstable importances and correlation matrix
  • Validate that feature importance changes after a data pipeline refactor and suggest rollbacks

FAQ

What methods does the skill inspect?

It covers model-specific importances, permutation importance, SHAP, and other common explainability outputs.

Do I need special tools to use the guidance?

You should have a standard ML environment (Python, common libraries) and access to model artifacts; the skill provides code snippets compatible with common stacks.