Package 'BinarybalancedCut' reference manual

Package 'BinarybalancedCut'

Title:	Threshold Cut Point of Probability for a Binary Classifier Model
Description:	Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.
Authors:	Navinkumar Nedunchezhian
Maintainer:	Navinkumar Nedunchezhian <[email protected]>
License:	GPL-2
Version:	0.2
Built:	2025-02-14 04:26:09 UTC
Source:	https://github.com/cran/BinarybalancedCut

Title:

Threshold Cut Point of Probability for a Binary Classifier Model

Description:

Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.

Authors:

Navinkumar Nedunchezhian

Maintainer:

Navinkumar Nedunchezhian <[email protected]>

License:

GPL-2

Version:

0.2

Built:

2025-02-14 04:26:09 UTC

Source:

https://github.com/cran/BinarybalancedCut

Help Index

This Supports the datascientist to determine the optimal threshold for binary classifier problem by visuallizing the sensitivity, specificity and accurarcy of the given model

Description

Prints 'Chart of sensitivity & specificity'.

Usage

Binary_threshold(probability,class)
Binary_threshold(probability,class)

Arguments

`probability`	Probability Obtained from the model
`class`	Actual Class of the datasets

Examples

set.seed(100);disease <- sample(c("yes","no"), 1000, replace=TRUE);
Probabilities<-sample(seq(0,1,by=0.01),1000,replace=TRUE);
Binary_threshold(Probabilities,disease)set.seed(100);disease <- sample(c("yes","no"), 1000, replace=TRUE);
Probabilities<-sample(seq(0,1,by=0.01),1000,replace=TRUE);
Binary_threshold(Probabilities,disease)