title: How to avoid overfitting in training data?
body: <p>I am a project in Bioinformatics where I have large training data set. It consists of around 18,000 positive and 1000 Negative instances. If I use this data as a training set then Positive instances will completely dominate over negative instances. So, during classification of test data, my negative data are also misclassified as positive.</p>
  
  <p>Is there any simple way to make a balance set of these positive and negative data to over come this problem?</p>