Feature addition/ subtraction and SVM model accuracy I am working on a text classification problem where I would like to improve the accuracy of my model. Presently, I am using SVM with linear SVC and OneVsRestClassifier. The model should correctly predict all of the subcategories for a parent category.

For entered account name, I should get the right description for my test data file that I will use to test the model later.



If I add another feature, +1 or -1, for debit and credit to my Account_Name field, will there any improvement in the accuracy?

Or, is there anything else that will help improve the accuracy of the model?

It is currently at 74% which is low.
It is hard to say without trying it. But you should add features as much as you can. When you have so many features, according to your success metrics, you can do future selection / regularization for both increasing the accuracy and speeding up the convergence of gradient descent. 

Also you can also try (after adding more features) other classification algorithms such as RandomForest, GBM, Xgboost etc - Tree based models are more accurate in recent years. 

Hope this is helpful!