Is this skill compatible with all types of machine learning models?

It is designed to work with a variety of models, including tree-based models and neural networks, by selecting the explanation technique best suited for the specific model architecture.

Can this skill help identify bias in my AI models?

Yes, by highlighting the most influential features behind a prediction, the tool helps developers detect if a model is relying on inappropriate or biased data points.

What techniques does this skill use for model explainability?

The skill utilizes industry-standard techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide robust insights into model behavior.

Does this tool provide visualizations for feature importance?

The skill analyzes data and can be integrated with visualization tools to present feature importance and prediction logic in a visual, easy-to-understand format.

ML Model Explainability Tool

Name: ML Model Explainability Tool
Author: Brmbobo

byBrmbobo

Ciencia de Datos y ML

Provides automated interpretability and explainability for machine learning models by analyzing feature importance and prediction logic.

Acerca de

The ML Model Explainability Tool empowers developers and data scientists to decode complex machine learning models through advanced interpretability techniques. By integrating tools like SHAP and LIME, this skill allows Claude to explain specific model predictions, identify the most influential features, and uncover hidden biases or unexpected feature interactions. It is an essential asset for anyone needing to debug model performance, ensure transparency in automated decision-making, or communicate technical model behavior to non-technical stakeholders in a clear, actionable format.

Características Principales

Automated local and global explanation generation using SHAP and LIME.
Identification of unexpected feature interactions for model debugging.
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Context-aware selection of the most appropriate explanation technique.
Structured reporting for communicating insights to non-technical stakeholders.
Detailed feature importance analysis to identify key model drivers.

Casos de Uso

Ensuring fairness and transparency by auditing models for biased feature influence.
Explaining automated decisions, such as loan rejections or insurance risk assessments.
Debugging model performance by identifying features causing incorrect predictions.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast explaining-machine-learning-models

For use in Claude.ai and ChatGPT

Download Skill

GitHub