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Explaining Machine Learning Models

explaining-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Replicate Model Automation

replicate-automation

ComposioHQ/awesome-claude-skills

Automate Replicate AI workflows via the Composio MCP integration to inspect model schemas, run predictions (sync or async), upload input files, list versions, and manage prediction history and related assets.

Survival Analysis Toolkit

scikit-survival

K-Dense-AI/claude-scientific-skills

Comprehensive scikit-survival guidance for handling censored data, building Cox, ensemble, and SVM survival models, and evaluating time-to-event predictions with concordance, AUC, and Brier scores, making it ideal for clinical or reliability studies in Python.

Shap Model Interpretation

K-Dense-AI/claude-scientific-skills

Explain machine learning predictions with SHAP values, generate global and local plots (beeswarm, waterfall, force), compare models, debug bias, and ship explainable AI in production.

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