What explanation techniques does this skill use?

The skill primarily leverages SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide robust model insights.

How does it help with model fairness?

By identifying feature importance and the weight of specific inputs, it helps users spot biases or unfair dependencies in the model's decision-making process.

Does it work with neural networks?

Yes, the skill supports various model types, including tree-based models, linear models, and neural networks, by selecting the appropriate interpretability technique.

Can I use this for non-technical stakeholders?

Yes, it is designed to translate complex model data into clear, concise explanations suitable for both developers and business stakeholders.

Model Explainability Tool

Name: Model Explainability Tool
Author: BbgnsurfTech

byBbgnsurfTech

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데이터 과학 및 ML

Provides interpretability and transparency for machine learning models by explaining predictions and identifying key feature importance.

소개

This skill empowers Claude to bridge the gap between complex black-box machine learning models and human-readable insights. By leveraging industry-standard techniques like SHAP and LIME, it allows users to understand the 'why' behind model predictions, debug performance issues, and ensure fairness. Whether you are a data scientist auditing a customer churn model or a developer explaining a loan rejection to stakeholders, this tool provides the analytical depth needed to interpret feature interactions and communicate model behavior effectively.

주요 기능

Calculates SHAP and LIME values for local and global model explanations
Identifies the most influential features driving specific model predictions
3 GitHub stars
Debugs model performance by uncovering unexpected feature interactions
Generates stakeholder-friendly reports on model behavior and transparency
Supports various model architectures including tree-based models and neural networks

사용 사례

Analyzing customer churn patterns to identify which behavioral factors most impact retention
Auditing financial models to explain rejection or approval decisions for regulatory compliance
Debugging machine learning models by investigating why specific outliers or incorrect predictions occur

소개

주요 기능

Calculates SHAP and LIME values for local and global model explanations
Identifies the most influential features driving specific model predictions
3 GitHub stars
Debugs model performance by uncovering unexpected feature interactions
Generates stakeholder-friendly reports on model behavior and transparency
Supports various model architectures including tree-based models and neural networks

사용 사례

Analyzing customer churn patterns to identify which behavioral factors most impact retention
Auditing financial models to explain rejection or approval decisions for regulatory compliance
Debugging machine learning models by investigating why specific outliers or incorrect predictions occur