ksplot

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

Riaz Khan

ROCit

Performance Assessment of Binary Classifier with Visualization

Md Riaz Ahmed Khan

Thomas Brandenburger

ksplot function

<dl><dt>object</dt>
<dd>An object of class <code>"rocit"</code>, returned by
<code>rocit</code> function.</dd>
<dt>...</dt>
<dd>Arguments to be passed to methods.
See <code>ksplot.rocit</code>.</dd></dl>

Arguments

KS Plot — ksplot

<dl>

<dt>object</dt>
<dd>An object of class <code>"rocit"</code>, returned by
<code>rocit</code> function.</dd>


<dt>...</dt>
<dd>Arguments to be passed to methods.
See <code>ksplot.rocit</code>.</dd>

</dl>

ksplot: KS Plot

Description

Usage

Arguments