I use the carriage train function to train SVM using the svmRadial kernel for the binary classification problem that I have.
When I run the train function on my data, I gradually add these messages that say
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
As soon as the code is finished (compilation / run?), I also received the following warnings:
> 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.
As can be seen from the above warnings, mentioning the NA values for some probabilistic calculations, why will these calculations fail?
As requested by @HFBrowning, an example of the data I use is given. I am trying to build a classifier to predict whether a telecommunications cell is either overload or inoperative (class).
> 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
Here is the code to control my train:
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
And here is my train function:
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")
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