我想使用caret::extractPrediction带有随机森林模型的函数来提取新的看不见的数据的预测，但我不知道为什么我的代码会引发错误Error: $ operator is invalid for atomic vectors。要使用此功能，应如何构造输入参数？
这是我的可复制代码：
library(caret)
dat <- as.data.frame(ChickWeight)
# create column set
dat$set <- rep("train", nrow(dat))
# split into train and validation set
set.seed(1)
dat[sample(nrow(dat), 50), which(colnames(dat) == "set")] <- "validation"
# predictors and response
all_preds <- dat[which(dat$set == "train"), which(names(dat) %in% c("Time", "Diet"))]
response <- dat[which(dat$set == "train"), which(names(dat) == "weight")]
# set train control parameters
contr <- caret::trainControl(method="repeatedcv", number=3, repeats=5)
# recursive feature elimination caret
set.seed(1)
model <- caret::train(x = all_preds,
y = response,
method ="rf",
ntree = 250,
metric = "RMSE",
trControl = contr)
# validation set
vali <- dat[which(dat$set == "validation"), ]
# not working
caret::extractPrediction(models = model, testX = vali[,-c(3,5,1)], testY = vali[,1])
caret::extractPrediction(models = model, testX = vali, testY = vali)
# works without problems
caret::predict.train(model, newdata = vali)
通过查看的文档，我找到了解决方案extractPrediction。基本上，该参数models不需要单个模型实例，而是一个模型列表。所以我只是插入list(my_rf = model)而不仅仅是model。
caret::extractPrediction(models = list(my_rf = model), testX = vali[,-c(3,5,1)], testY = vali[,1])
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我来说两句