gettptnfpfn-defunct

A response vector, consisting of only 1's and 0's.

Depth, up to which the positive predictions are made.

depth

npos

nneg

Function <code>gettptnfpfn</code> calculates the number of
true positive (TP), false positive (FP),
true negative (TN) and false negative (FN), if certain depth of the
observations are treated as positive. Generally it applies to the
observations that are rank-ordered with respect to some diagnostic score.

internal

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

gettptnfpfn-defunct function

<dl><dt>x</dt>
<dd>A response vector, consisting of only 1's and 0's.</dd>
<dt>depth</dt>
<dd>Depth, up to which the positive predictions are made.</dd>
<dt>npos</dt>
<dd>Number of positive responses.</dd>
<dt>nneg</dt>
<dd>Number of negative responses.</dd></dl>

Arguments

Get number of TP, FP, TN and FN — gettptnfpfn-defunct

<dl>

<dt>x</dt>
<dd>A response vector, consisting of only 1's and 0's.</dd>


<dt>depth</dt>
<dd>Depth, up to which the positive predictions are made.</dd>


<dt>npos</dt>
<dd>Number of positive responses.</dd>


<dt>nneg</dt>
<dd>Number of negative responses.</dd>

</dl>

gettptnfpfn-defunct: Get number of TP, FP, TN and FN

Description

Usage

Value

Arguments

Details

See Also