smbinning (version 0.9)

smbinning.metrics: Performance Metrics for a Classification Model

Description

It computes the classic performance metrics of a scoring model, including AUC, KS and all the relevant ones from the classification matrix at a specific threshold or cutoff.

Usage

smbinning.metrics(dataset, prediction, actualclass, cutoff = NA,
  report = 1, plot = "none", returndf = 0)

Arguments

dataset

Data frame.

prediction

Classifier. A value generated by a classification model (Must be numeric).

actualclass

Binary variable (0/1) that represents the actual class (Must be numeric).

cutoff

Point at wich the classifier splits (predicts) the actual class (Must be numeric). If not specified, it will be estimated by using the maximum value of Youden J (Sensitivity+Specificity-1). If not found in the data frame, it will take the closest lower value.

report

Indicator defined by user. 1: Show report (Default), 0: Do not show report.

plot

Specifies the plot to be shown for overall evaluation. It has three options: 'auc' shows the ROC curve, 'ks' shows the cumulative distribution of the actual class and its maximum difference (KS Statistic), and 'none' (Default).

returndf

Option for the user to save the data frame behind the metrics. 1: Show data frame, 0: Do not show (Default).

Value

The command smbinning.metrics returns a report with classic performance metrics of a classification model.

Examples

# NOT RUN {
# Load library and its dataset
library(smbinning) # Load package and its data

# Example: Metrics Credit Score 1
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=1) # Show report
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  cutoff=600, report=1) # User cutoff
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=0, plot="auc") # Plot AUC
smbinning.metrics(dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
                  report=0, plot="ks") # Plot KS

# Save table with all details of metrics
cbs1metrics=smbinning.metrics(
  dataset=smbsimdf1,prediction="cbs1",actualclass="fgood",
  report=0, returndf=1) # Save metrics details
# }