请考虑以下事项:
library(dplyr)
library(caret)
set.seed(42)
levels(iris$Species) <- list("setosa" = c("setosa"), "not_setosa" = c("versicolor", "virginica"))
train <- sample.int(0.5*nrow(iris))
test <- setdiff((1:nrow(iris)), train)
m <- train(factor(Species) ~ .,
data = iris %>% slice(train),
method = "regLogistic",
trControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 3),
tuneGrid = expand.grid(.cost = 1,
.loss = c("L1", "L2_dual", "L2_primal"),
.epsilon = seq(0.001, 0.01, length.out = 5)),
metric = "Accuracy",
preProcess = c("center", "scale"))
> m$bestTune
cost loss epsilon
1 1 L1 0.001
如何收集此处使用的L_1惩罚逻辑回归模型中使用的参数值(即系数值)?