title: SVM different results in R with same input and parameters
body: <p>I have developed a SVM model for fraud detection in a train dataset using the following parameters:</p>
  
  <pre><code>set.seed(1234)
  gamma.optimal &lt;- 0.02
  cost.optimal &lt;- 4
  svm_model1 &lt;- svm(log(response+0.00012345) ~ . , data_test, kernel="radial", gamma=gamma.opt, cost=cost.opt)
  </code></pre>
  
  <p>After creating the SVM, I evaluated my svm_model1 in a test data set to obtain the total fraud quantity:
  <code>sum(response)</code> and it was equal to 30.080 USD: </p>
  
  <pre><code>predictions &lt;- exp(predict(svm_model1 , testing))
  </code></pre>
  
  <p>This result was equal in a laptop (local mode with R GUI) and a small cluster using SparkR (4 nodes and 1 master with Cloudera 5.6).</p>
  
  <p>Happy with these results I tried to perform <strong>the same R script</strong> with <strong>the same test data set</strong>, <strong>the same svm_model1</strong> saved with <code>set.seed(1234)</code> in a .Rdata executable file, but this time in 2 different systems: Oracle BDA (6 slave nodes and 1 master) and another one with 4 slave nodes and Cloudera 5.7. </p>
  
  <p>The results in these 2 final systems were: <code>sum(response)</code> equal to 30.130 USD, using the same.</p>
  
  <pre><code>predictions &lt;- exp(predict(svm_model1 , testing))
  </code></pre>
  
  <p>My question is: </p>
  
  <p>1) If I used the same script, the same model saved in an executable file (.Rdata), and the same data; how is it possible that <code>e1071</code> svm with a radial kernel gives me different results?</p>
  
  <p>2) Are these results related with radial kernel nature's and parallel processing issues or different hardware characteristics? Or if you use <code>set.seed()</code> then no matter what hardware you have the result must be the same in R using <code>predict()</code> function?</p>
  
  <p>I thank you very much in advance for your time and help. Best regards.</p>