手动计算交叉验证会给出不同的结果

Question

让我们获取数据：

set.seed(42)
y <- rnorm(125)
x <- data.frame(runif(125), rexp(125))

我想对其进行 2 折交叉验证。所以：

library(caret)
model <- train(y ~ .,
  data = cbind(y, x), method = "lm",
  trControl = trainControl(method = "cv", number = 2)
)
model 

Linear Regression 

125 samples
  2 predictor

No pre-processing
Resampling: Cross-Validated (2 fold) 
Summary of sample sizes: 63, 62 
Resampling results:

  RMSE      Rsquared     MAE      
  1.091108  0.002550859  0.8472947

Tuning parameter 'intercept' was held constant at a value of TRUE

我想手动获取上面的这个 RMSE 值，以确保我完全理解交叉验证。

我目前的工作

正如我在上面看到的那样，我的样本分为两部分：62（1 折）和 63（2 折）。

#Training first model basing on first fold
model_1 <- lm(y[1:63] ~ ., data = x[1:63, ])
#Calculating RMSE for the first model
RMSE_1 <- RMSE(y[64:125], predict(model_1, newdata = x[64:125, ]))
#Training second model basing on second fold
model_2 <- lm(y[64:125] ~ ., data = x[64:125, ])
#Calculating RMSE for the second model
RMSE_2 <- RMSE(y[1:63], predict(model_1, newdata = x[1:63, ]))
mean(c(RMSE_1, RMSE_2))
 1.023411

我的问题是 - 为什么我得到不同的 RMSE？这个错误太大了，被视为估计错误——他们肯定是以另一种方式计算的。你知道我在做什么吗？

Answer 1

您使用的逻辑是正确的，但您需要进行两处更改：

Caret 将创建自己的 2 倍数据用于训练。它不会是 1:63、64:125，但插入符会根据种子
RMSE_2 中有错字，应该是 model_2

这是更新后的代码：

# the folds are kept in this part of the output (trial and error to find it haha)
model$control$index
f1 <- model$control$index[[1]]
f2 <- model$control$index[[2]]

# re-do your calculations but using the fold indexes, plus typo for RMSE_2
model_1 <- lm(y[f1] ~ ., data = x[f1, ])
#Calculating RMSE for the first model
RMSE_1 <- RMSE(y[f2], predict(model_1, newdata = x[f2, ]))
#Training second model basing on second fold
model_2 <- lm(y[f2] ~ ., data = x[f2, ])
#Calculating RMSE for the second model
RMSE_2 <- RMSE(y[f1], predict(model_2, newdata = x[f1, ]))

# matches now
mean(c(RMSE_1, RMSE_2))

手动计算交叉验证会给出不同的结果

Calculating cross validation manually gives different result

r

regression

linear-regression

cross-validation

manual-testing