Loan

A data containing information about 900 borrowers. It is a
modified version of publicly available real data.

datasets

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

Loan function

A data frame with 900 rows and 9 variables:<dl>
 <dt>Amount</dt>
<dd>Amount of loan, shown as
 percentage of a certain amount.</dd> <dt>Term</dt>
<dd>The number of payments on the loan.
 Values are in months.</dd> <dt>IntRate</dt>
<dd>Interest rate.</dd> <dt>ILR</dt>
<dd>Ratio of installment amount and total loan amount.</dd> <dt>EmpLen</dt>
<dd>Employment length, categorized.<ul>
<li><code>A:</code> 0-2 years</li>
<li><code>B:</code> 3-5 years</li>
<li><code>C:</code> 7-8 years</li>
<li><code>D:</code> 8+ years</li>
<li><code>U:</code> Unknown</li>
</ul></dd> <dt>Home</dt>
<dd>Status of home ownership.</dd> <dt>Income</dt>
<dd>Annual income.</dd> <dt>Status</dt>
<dd>A factor indicating whether the loan was fully paid
 (<code>FP</code>) or charged off (<code>CO</code>) after full term.</dd> <dt>Score</dt>
<dd>A risk score calculated from loan amount, interest rate and
 annual income. The log-odds of logistic regression were transformed into
 scores using \(PDO = 30\), \(OddsBase = 20\) and \(ScoreBase = 400\).
 See "References".</dd>
</dl>

Format

Loan Data — Loan

A data frame with 900 rows and 9 variables:<dl>
 <dt>Amount</dt>
<dd>Amount of loan, shown as
 percentage of a certain amount.</dd>

 <dt>Term</dt>
<dd>The number of payments on the loan.
 Values are in months.</dd>

 <dt>IntRate</dt>
<dd>Interest rate.</dd>

 <dt>ILR</dt>
<dd>Ratio of installment amount and total loan amount.</dd>

 <dt>EmpLen</dt>
<dd>Employment length, categorized.<ul>
<li><code>A:</code> 0-2 years</li>
<li><code>B:</code> 3-5 years</li>
<li><code>C:</code> 7-8 years</li>
<li><code>D:</code> 8+ years</li>
<li><code>U:</code> Unknown</li>
</ul></dd>

 <dt>Home</dt>
<dd>Status of home ownership.</dd>

 <dt>Income</dt>
<dd>Annual income.</dd>

 <dt>Status</dt>
<dd>A factor indicating whether the loan was fully paid
 (<code>FP</code>) or charged off (<code>CO</code>) after full term.</dd>

 <dt>Score</dt>
<dd>A risk score calculated from loan amount, interest rate and
 annual income. The log-odds of logistic regression were transformed into
 scores using \(PDO = 30\), \(OddsBase = 20\) and \(ScoreBase = 400\).
 See "References".</dd>


</dl>

Loan: Loan Data

Description

Usage

Arguments

Format

References

Examples