为什么我会收到与种子相关的错误消息？

Question

我用这个

中的例子

train$`1stFlrSF`<-train$S1stFlrSF 
train$`2ndFlrSF`<-train$S2ndFlrSF
train$`3SsnPorch`<-train$S3SsnPorch

library("randomForest")
set.seed(1)
rf.model <- randomForest(SalePrice ~ ., 
                         data = train,
                         ntree = 50,
                         nodesize = 5,
                         mtry = 2,
                         importance = TRUE, 
                         metric = "RMSE")

library("caret")
caret.oob.model <- train(train[,-ncol(train)], train$SalePrice, 
                         method = "rf",
                         ntree = 50,
                         tuneGrid = data.frame(mtry = 2),
                         nodesize = 5,
                         importance = TRUE, 
                         metric = "RMSE",
                         trControl = trainControl(method = "oob", seed = 1),
                         allowParallel = FALSE)

但是在caret.oob.model中出现错误

Error: Bad seeds: the seed object should be a list of length 2 with 1 integer vectors of size 1 and the last list element having at least a single integer.

这是我的数据集https://drive.google.com/file/d/1el-gAgA93EbYnM6VnDqzhT5c5uWsnKvq/view?usp=sharing

我该如何解决这个问题？

Answer 1

randomForest 是一种随机算法，它依赖于行和列的采样。设置 RNG 种子可获得可重现的结果。对于 randomForest 在调用训练函数之前只需要一个种子就足够了。在插入符中，由于重新采样以及安装了不止一个模型这一事实，事情变得更加复杂。

在您的情况下，即使没有重新采样，您也在拟合两个模型，一个用于 mtry 超参数的 OOB 评估和最终模型。

?trainControl 的帮助页面指出，seeds 参数是一组可选的整数，将用于在每次重采样迭代时设置种子。

它被指定为 B+1 个元素的列表，其中 B 是重采样的次数（“boot632”方法除外）。列表的前 B 个元素应该是长度为 M 的整数向量，其中 M 是正在评估的模型数（在您的情况下为 1）。列表的最后一个元素只需要是一个整数（对于最终模型）。

示例：

library(randomForest)
library(caret)

data(mtcars)

set.seed(1)
rf.model <- randomForest(mpg ~ ., 
                         data = mtcars,
                         ntree = 50,
                         nodesize = 5,
                         mtry = 2,
                         importance = TRUE, 
                         metric = "RMSE")

rf.model

Call:
 randomForest(formula = mpg ~ ., data = mtcars, ntree = 50, nodesize = 5,      mtry = 2, importance = TRUE, metric = "RMSE") 
               Type of random forest: regression
                     Number of trees: 50
No. of variables tried at each split: 2

          Mean of squared residuals: 7.353122
                    % Var explained: 79.1

caret.oob.model <- train(mpg ~ ., 
                         data = mtcars, 
                         method = "rf",
                         ntree = 50,
                         tuneGrid = data.frame(mtry = 2),
                         nodesize = 5,
                         importance = TRUE, 
                         metric = "RMSE",
                         trControl = trainControl(method = "oob", seeds = list(1, 1))) 

caret.oob.model$finalModel

Call:
 randomForest(x = x, y = y, ntree = 50, mtry = param$mtry, nodesize = 5,      importance = TRUE) 
               Type of random forest: regression
                     Number of trees: 50
No. of variables tried at each split: 2

          Mean of squared residuals: 7.353122
                    % Var explained: 79.1

在我看来，模型是相同的，基于完全相同的 Mean of squared residuals 和 % Var explained。

为什么我会收到与种子相关的错误消息？

Why am I getting an error related to seeds?

r

random-forest

r-caret