grnn.x_pfi: Derive the permutation feature importance of a predictor used...
In yager: Yet Another General Regression Neural Network

Description Usage Arguments Value See Also Examples

The function grnn.x_pfi derives the permutation feature importance (PFI) of a predictor used in the GRNN

1	grnn.x_pfi(net, i, class = FALSE, ntry = 1000, seed = 1)

`net`	The GRNN object generated by grnn.fit()
`i`	The ith predictor in the GRNN
`class`	TRUE or FALSE, whether it is for the classification or not
`ntry`	The number of random permutations to try, 1e3 times by default
`seed`	The seed value for the random permutation

A vector with the variable name and the PFI value.

grnn.x_imp

data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.x_pfi(net = gnet, 1)