# Is this training dataset enough for training and testing classification model?

My training dataset contains just 2 classes with 40 features.

In case 1, class 1 has 35 samples and class 2 has 700 samples.

In case 2, class 1 has 65 samples and class 2 has the same value as above.

Is my training dataset enough for constructing the model using SVM classifier or some other classifiers?

I'm using WEKA. Testing options are 10-fold cross-validation and %66 and i get very good results.

• You have an imballanced dara set in your scenario. To my best knowledge SVM's are not the "optimal" choice here... From my experience with text classification algorithms I would suggest a $k$-NN classifier with $k=$1. If you want to stick to SVM's think about the option to ballance your data set... – Unhandled exception Aug 4 '15 at 0:13
• k=1... really? Sounds inferior unless you have many doublets. Other models in general supersede k-NN. Try to listen to episode 13 of talking machines with Claudia Perlich. She has an interesting take on k-NN: thetalkingmachines.com/blog/2015/6/18/… – Soren Havelund Welling Aug 5 '15 at 6:53