Question: SVM caret training set confusion matrix

1

Matina •

**180**wrote:Hi guys,

I am using the caret package for binary classification on my RNA-seq data (59 samples x 15 features). I am trying to figure out the confusion matrix of the cross validation but I cant really seem to be able to find it.

Is this command correct in order to get the confusion matrix?

```
confusionMatrix(predict(final_svmFit_radial, new_data), class)
```

I am training the model using the following commands

```
fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
classProbs = TRUE,
savePred=TRUE,
returnResamp = "all",
summaryFunction = twoClassSummary)
set.seed(123)
final_svmFit_radial = train(class~., new_data,
method = "svmRadial",
trControl = fitControl,
prox=TRUE,
allowParallel=TRUE,
preProc = c("center", "scale"),
tuneLength = 9,
metric = "ROC")
```

**This are the resampling results**

```
> final_svmFit_radial
Support Vector Machines with Radial Basis Function Kernel
59 samples
15 predictors
2 classes: 'Cancer', 'Normal'
Pre-processing: centered (15), scaled (15)
Resampling: Cross-Validated (10 fold, repeated 10 times)
Summary of sample sizes: 53, 53, 53, 53, 54, 53, ...
Resampling results across tuning parameters:
C ROC Sens Spec ROC SD Sens SD Spec SD
0.25 1 0.9975 1 0 0.025 0
0.50 1 1.0000 1 0 0.000 0
1.00 1 1.0000 1 0 0.000 0
2.00 1 1.0000 1 0 0.000 0
4.00 1 1.0000 1 0 0.000 0
8.00 1 1.0000 1 0 0.000 0
16.00 1 1.0000 1 0 0.000 0
32.00 1 1.0000 1 0 0.000 0
64.00 1 1.0000 1 0 0.000 0
Tuning parameter 'sigma' was held constant at a value of 0.05198751
ROC was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.05198751 and C = 0.25.
```

**and this is the final model**

```
> final_svmFit_radial$finalModel
Support Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 0.25
Gaussian Radial Basis kernel function.
Hyperparameter : sigma = 0.0519875137214014
Number of Support Vectors : 45
Objective Function Value : -6.7424
Training error : 0.050847
Probability model included.
```

Thank you in advance! Matina

Actually I think the correct way to find the confusion matrix of the cross validation is like this, please correct me if im wrong

180