交叉验证集的插入符号 [英] caret coefficients of cross validated set
本文介绍了交叉验证集的插入符号的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
是否可以从R Caret包中获取所有交叉验证集的系数?
Is it possible to get the coefficients of all the cross validation set from R Caret package?
set.seed(1)
mu <- rep(0, 4)
Sigma <- matrix(.7, nrow=4, ncol=4)
diag(Sigma) <- 1
rawvars <- mvrnorm(n=1000, mu=mu, Sigma=Sigma)
d <- as.ordered(
as.numeric(rawvars[,1]>0.5) )
d[1:200] <- 1
df <- data.frame(rawvars, d)
ind <- sample(1:nrow(df), 500)
train <- df[ind,]
test <- df[-ind,]
trControl <- trainControl(method = "repeatedcv",
repeats = 1,
classProb = T,
summaryFunction= twoClassSummary)
fit.caret <- train(d~., data=train,
method="glm",
family = binomial(link="probit"), trControl =trControl)
> fit.caret$resample
ROC Sens Spec Resample
1 0.8383838 0.8148148 0.7272727 Fold01.Rep1
2 0.8881988 0.8571429 0.7391304 Fold02.Rep1
3 0.8937198 0.8518519 0.7826087 Fold03.Rep1
4 0.8792271 0.7407407 0.8260870 Fold04.Rep1
5 0.8771044 0.8888889 0.7727273 Fold05.Rep1
6 0.8703704 0.6666667 0.7727273 Fold06.Rep1
7 0.9145963 0.8928571 0.8260870 Fold07.Rep1
8 0.8649068 0.6785714 0.8260870 Fold08.Rep1
9 0.8084416 0.8928571 0.6818182 Fold09.Rep1
10 0.7938312 0.7857143 0.6363636 Fold10.Rep1
我有每次重复的ROC,但是每次运行我都需要系数
I have the ROCs for each repetition, but I also need coefficients for each run
推荐答案
不是。我们从保留的数据集中存储的内容很少。
Not really. We don't store much from the held-out data sets.
您可以编写自定义模型保存模型对象(通过保存
),您可以从那里获取系数。
You could write a custom model that saves the model object (via save
) and you can get the coefficients from there.
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