gainstable.rocit

A <code>"rocit"</code> object, created with <code><a rd-options="" href="/link/rocit?package=ROCit&version=2.1.1" data-mini-rdoc="ROCit::rocit">rocit</a></code>.

Number of desired groups in gains table. See
<code><a rd-options="" href="/link/gainstable.default?package=ROCit&version=2.1.1" data-mini-rdoc="ROCit::gainstable.default">gainstable.default</a></code>.

ngroup

Percentiles (in percentage) at which observations
should be separated to
form groups. See <code><a rd-options="" href="/link/gainstable.default?package=ROCit&version=2.1.1" data-mini-rdoc="ROCit::gainstable.default">gainstable.default</a></code>

breaks

<code>NULL</code>. Used for S3 generic/method consistency.

S3 method to create gains table from object of
class <code>"rocit"</code>.

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

gainstable.rocit function

<dl><dt>x</dt>
<dd>A <code>"rocit"</code> object, created with <code>rocit</code>.</dd>
<dt>ngroup</dt>
<dd>Number of desired groups in gains table. See
<code>gainstable.default</code>.</dd>
<dt>breaks</dt>
<dd>Percentiles (in percentage) at which observations
should be separated to
form groups. See <code>gainstable.default</code></dd>
<dt>...</dt>
<dd><code>NULL</code>. Used for S3 generic/method consistency.</dd></dl>

Arguments

Gains Table for Binary Classifier — gainstable.rocit

<dl>

<dt>x</dt>
<dd>A <code>"rocit"</code> object, created with <code>rocit</code>.</dd>


<dt>ngroup</dt>
<dd>Number of desired groups in gains table. See
<code>gainstable.default</code>.</dd>


<dt>breaks</dt>
<dd>Percentiles (in percentage) at which observations
should be separated to
form groups. See <code>gainstable.default</code></dd>


<dt>...</dt>
<dd><code>NULL</code>. Used for S3 generic/method consistency.</dd>

</dl>

gainstable.rocit: Gains Table for Binary Classifier

Description

Usage

Value

Arguments

Details

See Also

Examples