Attribute feature importance scores to explain machine learning model predictions.
npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill shap-jadzoghaib
Please help me install this Agent Skill. Skill: shap Source: https://github.com/jadzoghaib/Sabadell_Capstone/tree/main/.claude/skills/shap Command: npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill shap-jadzoghaib
SHAP provides a unified framework to interpret machine learning model predictions by attributing importance scores to individual features, making complex models transparent.
Use the shap library to compute and visualize feature contributions for your trained model and dataset, starting with beeswarm plots for global insights and waterfall plots for individual decisions.
High-intent search queries and answers about installing and using this skill.
Feature attribution interprets machine learning model predictions by quantifying how individual features influence specific outcomes, enabling transparent debugging and fairness analysis across tree-based, deep learning, and linear models.
Explaining model predictions for fraud detection involves calculating feature importance scores to distinguish key risk factors driving false positives versus true positives, often visualized using beeswarm and waterfall plots for clear diagnostics.
You need scikit-learn alongside pandas and scipy to train and prepare models, as computing feature importance scores requires a trained model and dataset to calculate accurate feature contributions and interactions.
SHAP values support deep learning and linear models by attributing importance scores to individual features, but accurate interpretation necessitates understanding model output types and selecting the appropriate explainer for diverse scenarios.
crazymsnTrain classical machine-learning models with scikit-learn pipelines and evaluation workflows.
tomlupoExplain model predictions by attributing outputs to input features with SHAP values.
Ritabrata-ChakrabortyGenerate statistical visualizations like scatter plots and heatmaps with Matplotlib and Pandas.
AlterLab-IEUExplain model predictions with SHAP values and attributions across model types.
