Holistic Multimodel Domain Analysis for Exploratory Machine
Learning
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Documentation for package ‘HMDA’ version 0.1
Help Pages
| best_of_family | Select Best Models by Performance Metrics |
| check_efa | Check Exploratory Factor Analysis Suitability |
| dictionary | Dictionary of Variable Attributes |
| hmda.adjust.params | Adjust Hyperparameter Combinations |
| hmda.autoEnsemble | Build Stacked Ensemble Model Using autoEnsemble R package |
| hmda.best.models | Select Best Models Across All Models in HMDA Grid |
| hmda.domain | compute and plot weighted mean SHAP contributions at group level (factors or domains) |
| hmda.efa | Perform Exploratory Factor Analysis with HMDA |
| hmda.feature.selection | Feature Selection Based on Weighted SHAP Values |
| hmda.grid | Tune Hyperparameter Grid for HMDA Framework |
| hmda.grid.analysis | Analyze Hyperparameter Grid Performance |
| hmda.init | Initialize or Restart H2O Cluster for HMDA Analysis |
| hmda.partition | Partition Data for HMDA Analysis |
| hmda.search.param | Search for Hyperparameters via Random Search |
| hmda.suggest.param | Suggest Hyperparameters for tuning HMDA Grids |
| hmda.wmshap | Compute Weighted Mean SHAP Values and Confidence Intervals via shapley algorithm |
| hmda.wmshap.table | Create SHAP Summary Table Based on the Given Criterion |
| list_hyperparameter | Create Hyperparameter List from a leaderboard dataset |
| suggest_mtries | Suggest Alternative mtries Values |