因此,我要进行此作业,其中必须创建3个不同的模型(r)。我可以单独解决这些问题。但是,我想更进一步,并创建一个函数来使用for循环训练所有函数。 (我知道我可以创建一个函数来每次训练3个模型。我不是在寻找其他解决问题的方法,我想这样做(或以类似的方式),因为现在我有3个模型,但可以想象是否我想训练20人!
我尝试创建一个列表来存储所有三个模型,但是我一直在发出一些警告。
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所以这是我创建的训练功能,并且可以正常工作,但是现在我想一次训练所有三个!
所以我尝试了这个:
library(caret)
library(readr)
library(rstudioapi)
library(e1071)
library(dplyr)
library(rpart)
TrainingFunction <- function(method,formula,data,tune) {
fitcontrol <- trainControl(method = "repeatedcv", repeats = 4)
if(method == "rf") {Model <- train(formula, data = data,method = method, trcontrol = fitcontrol , tunelenght = tune)}
else if (method == "knn"){
preObj <- preProcess(data[, c(13,14,15)], method=c("center", "scale"))
data <- predict(preObj, data)
Model <- train(formula, data = data,method = method, trcontrol = fitcontrol , tunelenght = tune)
}
else if (method == "svm"){Model <- svm(formula, data = data,cost=1000 , gamma = 0.001)}
Model
}
这是警告:
methods <- c("rf","knn","svm")
Models <- vector(mode = "list" , length = length(methods))
for(i in 1:length(methods))
{Models[i] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
当我建模时,输出是这样的:
Warning messages:
1: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
2: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
3: In svm.default(x, y, scale = scale, ..., na.action = na.action) :
Variable(s) ‘ProductType.GameConsole’ constant. Cannot scale data.
4: In Models[i] <- TrainingFunction(methods[i], Volume ~ ., List$trainingSet, :
number of items to replace is not a multiple of replacement length
答案 0 :(得分:1)
我认为问题出在这一行:
{Models[i] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
如果要将模型分配到列表的第i个位置,则应使用双括号,例如:
{Models[[i]] <- TrainingFunction(methods[i],Volume~.,List$trainingSet,5)}
另一种替代方法是使用lapply而不是显式循环,因此可以完全避免该问题:
train_from_method <- function(methods) {TrainingFunction(methods,Volume~.,List$trainingSet,5)}
Models <- lapply(species_vector, train_from_method)
答案 1 :(得分:0)
请考虑使用switch
来避免使用许多if
和else
,尤其是扩展到20个模型时。然后使用lapply
来建立一个没有初始化或迭代分配的列表:
TrainingFunction <- function(method, formula, data, tune) {
fitcontrol <- trainControl(method = "repeatedcv", repeats = 4)
Model <- switch(method,
"rf" = train(formula, data = data, method = method,
trcontrol = fitcontrol, tunelength = tune)
"knn" = {
preObj <- preProcess(data[,c(13,14,15)],
method=c("center", "scale"))
data <- predict(preObj, data)
train(formula, data = data, method = method,
trcontrol = fitcontrol, tunelength = tune)
}
"svm" = svm(formula, data = data, cost = 1000, gamma = 0.001)
)
}
methods <- c("rf","knn","svm")
Model_list <-lapply(methods, function(m)
TrainingFunction(m, Volume~., List$trainingSet, 5))