evaluate

evaluate.SCE

evaluate.SCA

Evaluate model performance for SCE or SCA models.

Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.

Kailong Li

Stepwise Clustered Ensemble

evaluate function

<dl> <dt>object</dt>
<dd>An SCE or SCA model object</dd> <dt>Testing_data</dt>
<dd>Testing dataset</dd> <dt>Training_data</dt>
<dd>Training dataset</dd> <dt>digits</dt>
<dd>Number of decimal places (default: 3)</dd> <dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Evaluate SCE and SCA Model Performance — evaluate

<dl>

 <dt>object</dt>
<dd>An SCE or SCA model object</dd>

 <dt>Testing_data</dt>
<dd>Testing dataset</dd>

 <dt>Training_data</dt>
<dd>Training dataset</dd>

 <dt>digits</dt>
<dd>Number of decimal places (default: 3)</dd>

 <dt>...</dt>
<dd>Additional arguments</dd>

</dl>

evaluate: Evaluate SCE and SCA Model Performance

Description

Usage

Value

Arguments

See Also