title: LIBSVM easy.py and grid.py doing worse that svm-train
body: <p>I am facing a problem with libsvm and I am hoping you can help me</p>
  
  <p>When I use svm-train.exe with default parameters like this...</p>
  
  <pre><code>svm-train  dikomou
  svm-predict  dikomou.t  dikomou.model   dikomou.t.predict
  </code></pre>
  
  <p>I get accuracy  84.72%</p>
  
  <p>When I use scaling  [-1, 1] ,and same scaling for training and testing file like this....</p>
  
  <pre><code>svm-scale -l -1 -u 1 -s range1 dikomou &gt; dikomou.scale
  svm-scale -r range1 dikomou.t &gt; dikomou.t.scale
  svm-train dikomou.scale
  svm-predict dikomou.t.scale dikomou.scale.model dikomou.t.predict
  </code></pre>
  
  <p>I get less accuracy  81.94%</p>
  
  <p>If I do the scaling for 0 to 1  I get accuracy  87.5%</p>
  
  <p>So I keep the 0 to 1 scaling.</p>
  
  <p>BUT when I use <code>grid.py</code> with the 0 to 1 scaled data like this</p>
  
  <pre><code>grid.py dikomou.scale
  .. 
  8  0.0078125   84 ,25 
  
  $ ./svm-train -c 8 -g 0.0078125 dikomou.scale
  $ ./svm-predict dikomou.t.scale dikomou.scale.model dikomou.t.predict
  </code></pre>
  
  <p>I get cross validation rate 84.25% and total accuracy of 79.166%  and best given c = 8 gamma= 0.0078125</p>
  
  <p>So the <code>grid.py</code> gives me less!!! accuracy  than the one using the svm train with defaults. So I have two questions.</p>
  
  <ol>
  <li>How is this possible?? </li>
  <li>What are the default values of <code>c</code> and <code>gamma</code> the svm train uses?? (I cant find this clearly in the documentation. is gamma 1/number of features   and  c = 1?) and why the do better than when I use the <code>grid.py</code>?</li>
  </ol>
  
  <p><code>easy.py</code> also gives me worse results than the defaults. what can i do?</p>