I am doing classification of "text quality" using four classes and using 30 features with 1300 samples. I am using the following classifiers:

  1. LDAC based on linear discriminant analysis from mlpy.
  2. svm with rbf and gridsearch
  3. svm with polynomial kernel degree 5 and gridsearch and
  4. 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!

  • 3
    $\begingroup$ It's possible that your features just aren't strongly related to your classes, at least in this sample, and that the relationship that does exist is well captured by the linear methods. $\endgroup$
    – Peter Flom
    Nov 13, 2012 at 11:16
  • $\begingroup$ If you have four classes, guessing will give you 25% accuracy as a baseline. Thus, if you get up to nearly 60%, I would not call that poor. $\endgroup$
    – bayerj
    Nov 13, 2012 at 19:39
  • $\begingroup$ Whether the results are useful or not depends on the distribution of your outcome. If the most common class occurs ~55-60% then your results are useless. If on the other hand the classes are uniformly distributed (25% each), then 57-58% is not bad. $\endgroup$
    – George
    Nov 21, 2015 at 12:30
  • 1
    $\begingroup$ How are you defining poor? What baseline are you using? Is there some reference baseline from the literature? Are you comparing it to a simple method like k-means, etc? Also have you tried doing any data pre-processing like zero-centering or normalization? $\endgroup$
    – Indie AI
    Dec 21, 2015 at 18:04

1 Answer 1


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.


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