# What method/classifier should I use for a training set with lots of attributes but few examples

Each training example has 100 numeric attributes plus one output class and about 80% of the attributes are 'zero' (means no data collected). The value of attributes varies in a small range, like $(-20,20)$. I have 100 examples like this. What method/classifier should I use? I tried KNN, Naive Bayes, SVM, random forest/tree, none of these methods give me accuracy above 50% (I used 10-fold cross validation). What should I do?

• Did you tune the parameters on the SVM? How many trees are you using in your forest? Please add more details regarding the implementation of the algorithms. – Firebug Aug 22 '16 at 20:27
• I tried 100 trees,got 44% accuracy,10 trees, got 33% and 200 trees got 40%. For SVM what specific parameter are you looking for? – tikael Aug 24 '16 at 15:21

This is the well known large $p$ small $n$ problem, often named as $p \gg n$, common in biological, biomedical and imaging problems, basically any field where either data is scarce/expensive to come by or simply carries too much information.