Extract Most "Important" Predictors (Experimental)
Extract the most "important" predictors for regression and classification models.
topPredictors(object, n = 1L, ...) ## Default S3 method: topPredictors(object, n = 1L, ...) ## S3 method for class 'train' topPredictors(object, n = 1L, ...)
object |
A fitted model object of appropriate class (e.g., |
n |
Integer specifying the number of predictors to return. Default is
|
... |
Additional optional arguments to be passed onto
|
This function uses the generic function varImp to
calculate variable importance scores for each predictor. After that, they are
sorted at the names of the n highest scoring predictors are returned.
## Not run:
#
# Regression example (requires randomForest package to run)
#
Load required packages
library(ggplot2)
library(randomForest)
# Fit a random forest to the mtcars dataset
data(mtcars, package = "datasets")
set.seed(101)
mtcars.rf <- randomForest(mpg ~ ., data = mtcars, mtry = 5, importance = TRUE)
# Topfour predictors
top4 <- topPredictors(mtcars.rf, n = 4)
# Construct partial dependence functions for top four predictors
pd <- NULL
for (i in top4) {
tmp <- partial(mtcars.rf, pred.var = i)
names(tmp) <- c("x", "y")
pd <- rbind(pd, cbind(tmp, predictor = i))
}
# Display partial dependence functions
ggplot(pd, aes(x, y)) +
geom_line() +
facet_wrap(~ predictor, scales = "free") +
theme_bw() +
ylab("mpg")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.