andikarachman

andikarachman/data-science-plugin

Skill Claude CodeCodex

SHAP API patterns for model interpretability -- explainer selection, feature attribution, and visualization. Use when /ds:experiment needs per-prediction explanations, global feature importance, or interaction analysis. For built-in tree importance and permutation importance use scikit-learn; for coefficient-based…

andikarachman/data-science-plugin

Skill Claude CodeCodex

Select and implement appropriate train/validation/test split strategies based on data characteristics. Use when designing the evaluation framework for a model.

andikarachman/data-science-plugin

Skill Claude CodeCodex

Guided statistical analysis with test selection, assumption checking, power analysis, and APA reporting. Use when /ds:experiment needs to design comparison protocols, validate assumptions, or report results.

andikarachman/data-science-plugin

Skill Claude CodeCodex

Statsmodels API patterns for OLS, GLM, discrete choice, time series (ARIMA/SARIMAX), and diagnostics. Use when /ds:experiment needs statsmodels model fitting, diagnostics, or time-series forecasting, or /ds:eda needs VIF and stationarity checks. For guided test selection and APA reporting use statistical-analysis.

andikarachman/data-science-plugin

Skill Claude CodeCodex

Detect target leakage in feature sets by checking temporal validity, feature-target correlation, and information flow. Use before training any model.

andikarachman/data-science-plugin

Skill Claude CodeCodex

Hyperparameter tuning workflow reference -- strategy selection, Bayesian optimization with Optuna, search space design, and result analysis. Use when /ds:experiment needs to choose a tuning strategy, design search spaces, or analyze tuning runs.