title: finding good C and gamma parameters for SVR in libsvm
body: <p>Hi everybody I have difficulties using SVR in libsvm.
  I am following all steps for finding best C and gamma parameter by using grid.py but the accuracy rate still quite low, around 0.1. But when I tried using cross validation the accuracy is 0.99. I'm still finding a way to improve the accuracy rate.
  Because I'm using svr the accuracy that I'm talking about here is squared correlation coefficient value. 
  My data contain 30 x 5 predictors and 30 x 1 predicted value (data label).
  Any suggestion will be appreciated. </p>
  
  <p>What I've tried:</p>
  
  <p>Finding the best <code>C</code> and <code>g</code> values:</p>
  
  <pre><code>tools/grid.py train.txt.scale
  </code></pre>
  
  <p>and I got: <code>C = 16</code>, <code>g = 0.008</code></p>
  
  <p>Run 30-fold cross validation (I want to use leave one out cross validation) with the best <code>C</code> and <code>g</code> values:</p>
  
  <pre><code>svm-train -v 30 -c 16 -g 0.008 train.txt
  </code></pre>
  
  <p>I got:</p>
  
  <p>Cross Validation Mean squared error = 6.62503
  Cross Validation Squared correlation coefficient = 0.38175</p>
  
  <p>But on <code>svmpredict</code>, I got 0.103281159524459 accuracy.</p>
  
  <p>It seem this are common methods in libsvm to find best <code>C</code> and <code>g</code> values. I just really don't know if there's any hope to improve this accuracy to 0.8 or higher.</p>