Which types of machine learning models are supported?

It supports a wide range of models, including tree-based algorithms like XGBoost and Random Forest, as well as complex neural networks.

How does this help with model debugging?

By showing which features contribute most to a prediction, you can identify if your model is over-fitting on noise or relying on unintended data leakages.

What interpretability techniques does this skill use?

This skill primarily utilizes SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide both local and global model insights.

Can I use this skill to explain models to non-technical users?

Yes, it is specifically designed to translate complex mathematical interactions into clear, feature-based explanations suitable for stakeholders and business users.

Machine Learning Model Explainability

Name: Machine Learning Model Explainability
Author: intent-solutions-io

byintent-solutions-io

データサイエンスとML

Provides deep insights into machine learning model predictions and behavior using interpretability techniques like SHAP and LIME.

概要

The Machine Learning Model Explainability skill empowers developers and data scientists to decode the logic behind AI model predictions. By leveraging industry-standard techniques like SHAP and LIME, it identifies influential features, clarifies decision-making processes, and helps diagnose performance issues. This skill is essential for ensuring transparency, debugging unexpected model behavior, and communicating complex technical insights to non-technical stakeholders in a clear, actionable format.

主な機能

Automated feature importance analysis using SHAP and LIME
0 GitHub stars
Support for various architectures including tree-based models and neural networks
Detailed explanations for individual model predictions (local interpretability)
Global model behavior analysis to identify dominant trends
Fairness and bias detection through feature contribution transparency

ユースケース

Debugging model performance by analyzing unexpected feature interactions
Identifying primary drivers behind customer churn or sales forecast trends
Explaining specific outcomes like loan rejections or insurance premiums for regulatory compliance

概要

主な機能

Automated feature importance analysis using SHAP and LIME
0 GitHub stars
Support for various architectures including tree-based models and neural networks
Detailed explanations for individual model predictions (local interpretability)
Global model behavior analysis to identify dominant trends
Fairness and bias detection through feature contribution transparency

ユースケース

Debugging model performance by analyzing unexpected feature interactions
Identifying primary drivers behind customer churn or sales forecast trends
Explaining specific outcomes like loan rejections or insurance premiums for regulatory compliance