mAPKL (version 1.4.2)

metrics: Computes several clasification metrics

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

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

Examples

## 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)