Holistic Multimodel Domain Analysis for Exploratory Machine Learning

Documentation for package ‘HMDA’ version 0.2.0

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.compare.shap.plot	Compare SHAP plots across selected models
hmda.domain	Domain-level WMSHAP summary and plot
hmda.efa	Perform Exploratory Factor Analysis with HMDA
hmda.feature.selection	Feature Selection Based on Weighted SHAP Values
hmda.grid	Tune a Cartesian Hyperparameter Grid in HMDA
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.plot	Plot WMSHAP contributions
hmda.plot.metrics	Plot model performance metrics across a grid of models
hmda.search.param	Search for Hyperparameters via Random Search
hmda.suggest.param	Suggest Hyperparameters for tuning HMDA Grids
hmda.test	Normalize a vector based on specified minimum and maximum values
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
suggest_mtries	Suggest Alternative mtries Values