How does this skill help with model debugging?

It provides workflows to identify data leakage by flagging features with suspiciously high importance and helps analyze misclassified samples to understand where the model logic fails.

Which machine learning frameworks are compatible with this SHAP skill?

This skill supports all major frameworks including tree-based models like XGBoost and LightGBM, deep learning frameworks like TensorFlow and PyTorch, and scikit-learn linear models.

What is the best SHAP explainer for speed?

LinearExplainer and TreeExplainer are the fastest. For neural networks, DeepExplainer is optimized for performance, while KernelExplainer is used as a slower, model-agnostic fallback.

Can I use SHAP to explain individual model predictions?

Yes, SHAP is excellent for local explanations. You can use waterfall or force plots to see exactly how each feature contributed to a specific output compared to the baseline.

SHAP Model Interpretability

Name: SHAP Model Interpretability
Author: ricable

byricable

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Ciencia de Datos y ML

Explains machine learning model predictions and feature importance using mathematically grounded Shapley values and high-impact visualizations.

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The SHAP skill enables developers and data scientists to implement explainable AI (XAI) by providing clear insights into how specific features influence machine learning model outputs. By leveraging Shapley values from cooperative game theory, it offers a unified approach to interpret complex architectures—including XGBoost, LightGBM, PyTorch, and TensorFlow. Whether you need to debug model behavior, identify data leakage, or validate fairness across demographic groups, this skill provides the necessary workflows and visualization patterns to move from black-box models to transparent, trustworthy AI systems.

Características Principales

Rich visualization library featuring beeswarm, waterfall, force, and scatter plots.
Advanced debugging workflows to identify misclassifications and unexpected feature interactions.
1 GitHub stars
Global and local feature importance analysis for comprehensive model understanding.
Integrated fairness and bias analysis tools for ethical AI development.
Unified explainers for Tree, Deep Learning, Linear, and model-agnostic black-box architectures.

Casos de Uso

Explaining high-stakes individual predictions to stakeholders in finance or healthcare.
Auditing models for bias by comparing feature attributions across protected attributes.
Optimizing feature engineering by identifying non-linear relationships and feature interactions.

Acerca de

Características Principales

Rich visualization library featuring beeswarm, waterfall, force, and scatter plots.
Advanced debugging workflows to identify misclassifications and unexpected feature interactions.
1 GitHub stars
Global and local feature importance analysis for comprehensive model understanding.
Integrated fairness and bias analysis tools for ethical AI development.
Unified explainers for Tree, Deep Learning, Linear, and model-agnostic black-box architectures.

Casos de Uso

Explaining high-stakes individual predictions to stakeholders in finance or healthcare.
Auditing models for bias by comparing feature attributions across protected attributes.
Optimizing feature engineering by identifying non-linear relationships and feature interactions.