似乎在caret
训练模型时,你几乎被迫进行参数调整。我知道这通常是一个好主意,但如果我想在训练时明确说明模型参数怎么办?
svm.nf <- train(y ~ .,
data = nf,
method = "svmRadial",
C = 4, sigma = 0.25, tuneLength = 0)
出了点问题;缺少所有RMSE指标值:
RMSE Rsquared
Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA
Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA
NA's :2 NA's :2
train.default(x,y,weights = w,...)出错:停止 另外:警告信息: 在nominalTrainWorkflow中(x = x,y = y,wts = weights,info = trainInfo,: 重新抽样的绩效指标中缺少值。
答案 0 :(得分:3)
params <- data.frame(C = 4,sigma=.25)
> params
C sigma
1 4 0.25
svm.nf <- train(Point_diff ~ .,
data = nf,
method = "svmRadial",
tuneGrid=params)
> svm.nf
Support Vector Machines with Radial Basis Function Kernel
1248 samples
14 predictor
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 1248, 1248, 1248, 1248, 1248, 1248, ...
Resampling results:
RMSE Rsquared
15.53451 0.0550965
Tuning parameter 'sigma' was held constant at a value of 0.25
Tuning parameter 'C'was held constant at a value of 4