list_hyperparameter

Detects columns in a data frame that contain
 hyperparameters for H2O DRF/GBM algorithms and returns a list
 with the unique values from each parameter column.

Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) <doi:10.13140/RG.2.2.32473.63846>.

E. F. Haghish 

HMDA

Holistic Multimodel Domain Analysis for Exploratory Machine
Learning

list_hyperparameter function

<dl><dt>df</dt>
<dd>A data frame containing model results with
hyperparameter columns.</dd></dl>

Arguments

Author

Create Hyperparameter List from a leaderboard dataset — list_hyperparameter

<dl>

<dt>df</dt>
<dd>A data frame containing model results with
hyperparameter columns.</dd>

</dl>

list_hyperparameter: Create Hyperparameter List from a leaderboard dataset

Description

Usage

Value

Arguments

Author

Details