metrics {mAPKL} | R Documentation

## 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

### Author(s)

Argiris Sakellariou

### 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)

[Package mAPKL version 1.16.0 Index]