getEvaluationMeasures: Calculate Evaluation Measures

In KelseyChetnik/MetaClean: Detection of Low-Quality Peaks in Untargeted Metabolomics Data

Description

Calculate evaluation measures using the predictions generated during cross-validation.

Usage

getEvaluationMeasures(models, k, repNum)

Arguments

models	list. A list of trained models, like that returned by trainClassifiers()
k	integer. Number of folds used in cross-validation
repNum	integer. Number of cross-validation rounds

Value

A dataframe with the following columns: Model, RepNum, Pass_FScore, Pass_Recall, Pass_Precision, Fail_FScore, Fail_Recall, Fail_Precision, Accuracy

Examples

# calculate all seven evaluation measures for each model and each round of cross-validation
evalMeasuresDF <- getEvaluationMeasures(models=models, k=5, repNum=10)

KelseyChetnik/MetaClean documentation built on May 17, 2021, 5:33 a.m.