metrics: Computes several clasification metrics

Description Usage Arguments Value Author(s) Examples

Description

This function calculates several classification related metrics. It uses the original and the predicted samples' labels to quantify the quality of the classification process. Those meassures give us a direct outlook of the selected "genes" and how well discriminate between two phenotypes.

Usage

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    metrics(classLbls, predLbls)

Arguments

classLbls

The initial class labels.

predLbls

The predicted class labels.

Value

AUC

The Area Under the ROC curve as a degree of samples discrimination

Accuracy

The classification accuracy

MCC

The MCC classification meassure

Specificity

The degree of true negative's identification

Sensitivity

The degree of true positive's identification

Author(s)

Argiris Sakellariou

Examples

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## Suppose 'val' represent the correct validation set labels
## and 'predictions' the predicted labels according to an SVM model

    val <- c(rep(0,8),rep(1,4))
    predictions <- c(rep(0,6),1,1,rep(1,3),0)
    perfMetrics <- metrics(classLbls=val, predLbls=predictions)

asakellariou/git-git.bioconductor.org-packages-mAPKL documentation built on June 5, 2019, 8:49 p.m.