我正在使用插入符号中的train函数来训练SVM，使用svmRadial内核进行二进制分类任务。
当我在我的数据上运行train功能时，我会逐渐收到这些消息，说明
line search fails -2.13865 -0.1759025 1.01927e-05 3.812143e-06 -5.240749e-08 -1.810113e-08 -6.03178e-13line search fails -0.7148131 0.1612894 2.32937e-05 3.518543e-06 -1.821269e-08 -1.37704e-08 -4.726926e-13
代码完成后（编译/运行？）我还收到了以下警告：
> warnings()
Warning messages:
1: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
2: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
3: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
4: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
5: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
6: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
7: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
8: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
9: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
10: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
11: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
12: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
13: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
14: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
15: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
16: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
17: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
18: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
19: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
20: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
21: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
22: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
23: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
24: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
25: In method$predict(modelFit = modelFit, newdata = newdata, ... :
kernlab class prediction calculations failed; returning NAs
26: In method$prob(modelFit = modelFit, newdata = newdata, ... :
kernlab class probability calculations failed; returning NAs
27: In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
28: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, ... :
There were missing values in resampled performance measures.
从上面的警告可以看出，有些概率计算会提到NA值，为什么这些计算失败？
根据@HFBrowning请求，这里是我正在使用的数据的示例。我正在尝试建立一个分类器，以预测电信小区是否过冲或过度。（过去）。
> head(imbal_training,10)
Total.Tx.Height Antenna.Tilt Antenna.Gain Ant.Vert.Beamwidth RTWP Voice.Drops Range Max.Distance Rural Suburban Urban
2 31.25 0 15.9 10.0 -103.55396 12 5.14 6.24 1 0 0
5 31.25 0 18.2 4.4 -104.76192 1 3.88 4.98 1 0 0
7 25.14 4 15.9 9.6 -102.93839 1 6.58 9.17 1 0 0
9 25.14 2 18.8 4.3 -104.23198 4 5.08 7.67 1 0 0
11 10.66 4 16.2 10.0 -98.23691 17 23.33 24.69 0 1 0
12 10.66 6 16.2 10.0 -103.78522 5 18.24 19.60 0 1 0
13 10.66 5 16.2 10.0 -94.59940 5 20.20 21.56 0 1 0
14 10.66 3 18.7 4.4 -103.17622 3 23.86 25.22 0 1 0
15 10.66 5 18.7 4.4 -104.97827 0 23.86 25.22 0 1 0
16 10.66 4 18.8 4.4 -105.78948 1 23.86 25.22 0 1 0
Class HSUPA.Throughput Max.HSDPA.Users HS.DSCH.throughput Max.HSUPA.Users Avg.CQI
2 Not.Overshooting 222.62 16 2345.54 25 17.99
5 Overshooting 263.83 8 3894.07 13 21.82
7 Overshooting 392.66 14 5134.80 15 23.00
9 Overshooting 478.58 8 7203.39 8 24.70
11 Overshooting 173.21 11 2429.06 15 23.51
12 Overshooting 210.61 16 2694.93 20 19.76
13 Overshooting 205.81 11 3278.06 13 22.10
14 Overshooting 394.10 10 3881.88 13 25.01
15 Overshooting 371.71 10 3765.10 13 23.33
16 Overshooting 321.32 6 4422.15 8 24.85
以下是我列车控制的代码：
#run the algorithms using 10 fold cross validation
set.seed(123)
train_Control <- trainControl(method = "repeatedCV",
number = 10,
repeats = 3,
savePredictions = T,
classProbs = T, #required for the ROC curve calcs
summaryFunction = twoClassSummary) #uses AUC to pick the best model
这是我的火车功能：
#uses the rose_training dataset with a kernel model
set.seed(123)
fit.rose.Kernel <- train(Class ~ Total.Tx.Height +
Antenna.Tilt +
Antenna.Gain +
Ant.Vert.Beamwidth +
RTWP +
Voice.Drops +
Range +
Max.Distance +
Rural +
Suburban +
Urban +
HSUPA.Throughput +
Max.HSDPA.Users +
HS.DSCH.throughput +
Max.HSUPA.Users +
Avg.CQI,
data = rose_train,
method = 'svmRadial',
preProcess = c('center','scale'),
trControl=train_Control,
tuneLength=15,
metric = "ROC")
为了更好地理解代码的哪个部分导致问题，我清除了所有现有的警告并逐个运行每个模型以查看它在哪里标记。
最初我将444到469行标记为有问题的部分，但今天这部分没有任何警告。现在接下来几行正在吐出与前一天相同的警告，但是没有任何改变，期望清除警告。
总之，我想要比较两种类型的模型，使用svmLinear的线性SVM和使用smvRadial的内核模型。
对于这两种型号，我使用不同配置的训练数据，因为我的原始数据集严重失衡为“过冲”（~80 / 20）。我使用原始的不平衡数据，然后进行下采样，上采样，使用SMOTE和ROSE生成合成数据，以使用每种类型的训练集训练线性和内核模型。
有谁知道这些行搜索失败并且警告指的是什么？
为了提供可重现的示例，here是指向我的代码副本的链接，here是我正在使用的数据集的输出版本。导致这些消息和警告的代码部分从第444行开始。
如果有人能提供一些帮助，我将非常感激。