I'm new in R. I have a problem with classification using SVM. In my train_data, I have 82 samples and 25770 features while in my test_data I have 36 samples and 25770 features. I would like to make classification using 10 top features that I gained from Feature Selection. From feature selection, I have this:
(see attached file)
And this my code for classification:
training_dataset
I suggest you to follow https://stackoverflow.com/questions/41592661/determining-the-most-contributing-features-for-svm-classifier-in-sklearn for a bit enlightenment.
https://towardsdatascience.com/svm-feature-selection-and-kernels-840781cc1a6c
Having a very high Accuracy and a low AUC is a very strong indicator that your model may have Data Imbalance (unbalanced dataset). Thus, you may need to apply data balancing techniques/strategies like over-sampling or under-sampling prior to training and testing/validating your model.
Additionally, other classification fitness/utility functions like Precision, Recall, and F1-Score can be employed herein to further give you a clearer direction and indication on how well your model is learning and progressing with respect to the classification task.
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