Title: | ROC Curves for Multi-Class Analysis |
---|---|
Description: | Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029). |
Authors: | Marton Varga [cre, aut] |
Maintainer: | Marton Varga <[email protected]> |
License: | GPL-3 |
Version: | 0.1.0 |
Built: | 2024-11-12 06:29:55 UTC |
Source: | CRAN |
Function 'multiroc' can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both one-vs-one and one-vs-all approaches.
multiroc(y, x, k, type = c("OvO", "OvA"), plot = TRUE, data)
multiroc(y, x, k, type = c("OvO", "OvA"), plot = TRUE, data)
y |
A string, dependent variable |
x |
A vector of strings, independent variables |
k |
The number of categories |
type |
A string, "OvO" for one-vs-one, "OvA" for one-vs-all approach |
plot |
A logical, TRUE for the plot of the curves, FALSE for the average AUC |
data |
A data frame, the dataset to use |
plot with ROC curves using ggroc, pROC (if plot=TRUE) or the average AUC (if plot=FALSE)
multiroc(y="Species", x=c("Petal.Width","Petal.Length","Sepal.Width","Sepal.Length"), k=3, type=("OvA"), plot=TRUE, data=iris) multiroc(y="Species", x=c("Petal.Width","Petal.Length","Sepal.Width","Sepal.Length"), k=3, type=("OvO"), plot=FALSE, data=iris)
multiroc(y="Species", x=c("Petal.Width","Petal.Length","Sepal.Width","Sepal.Length"), k=3, type=("OvA"), plot=TRUE, data=iris) multiroc(y="Species", x=c("Petal.Width","Petal.Length","Sepal.Width","Sepal.Length"), k=3, type=("OvO"), plot=FALSE, data=iris)