Q: Same (poor) results with linear and non-linear classifiers I am doing classification of "text quality" using four classes and using 30 features with 1300 samples. I am using the following classifiers: 


*

*LDAC based on linear discriminant analysis from mlpy. 

*svm with rbf and gridsearch 

*svm with polynomial kernel degree 5 and gridsearch and 

*random forest. 


The svm and random forest classifiers are based on scikit-learn.
I am getting similar (poor) accuracy results with all the classifiers. I get approx 57.1% with LDAC, svm and random forests vary from 57.8- 58.1%. The accuracy is based on cross validation and the corresponding confusion matrices (sum of diagonal/total). 
Why am I getting similar results with all the classifiers? Any suggestions about the reasons for having similar results when using linear and non-linear classfiers? Is this agreement between linear/non-linear classifiers at low-level a strong hint of flaws in some specific area? I appreciate any comments here in order to try to improve this situation!

A: First of all you need to understand whether your features are really doing their job and help separate your classes. For this sake many instruments are available, take a look at Orange ( http://www.biolab.si/supp/bi-vizrank/gui.htmu ) just to name a few.
Typically, these instruments will hint you about most correct classifier.
Without seeing your data any conclusions are very uncertain.
And yes, if you indeed have only two classes, having 60% accuracy is not so bad on its own. 
