From jeremylongshore-claude-code-plugins-plus-skills
Analyzes feature importance in machine learning models during training. Provides step-by-step guidance, production-ready code, and best practices for sklearn, PyTorch, TensorFlow.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin langchain-py-packThis skill is limited to using the following tools:
This skill provides automated assistance for feature importance analyzer tasks within the ML Training domain.
Guides DataRobot feature engineering: discover automated features, analyze importance, optimize sets, and document for ML models.
Generates and executes Python code to create, select, and transform ML features including interactions, scaling, encoding, and importance analysis.
Explains ML model predictions using SHAP, LIME, and feature importance to identify influential features and debug behavior.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for feature importance analyzer tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with feature importance analyzer" Result: Provides step-by-step guidance and generates appropriate configurations
| 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 |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn