R caret extractPrediction with random forest model: Error: $ operator is invalid for atomic vectors
R caret extractPrediction with random forest model: Error: $ operator is invalid for atomic vectors
我想使用函数 caret::extractPrediction
和随机森林模型提取对新的未见数据的预测,但我无法弄清楚为什么我的代码会抛出错误 Error: $ operator is invalid for atomic vectors
。输入参数应该如何构造才能使用此功能?
这是我的可重现代码:
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)
我通过查看 extractPrediction
的文档找到了解决方案。基本上,参数 models
不需要单个模型实例,而是模型列表。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])
我想使用函数 caret::extractPrediction
和随机森林模型提取对新的未见数据的预测,但我无法弄清楚为什么我的代码会抛出错误 Error: $ operator is invalid for atomic vectors
。输入参数应该如何构造才能使用此功能?
这是我的可重现代码:
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)
我通过查看 extractPrediction
的文档找到了解决方案。基本上,参数 models
不需要单个模型实例,而是模型列表。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])