title: Weka Resample to balance instances in binary dataset
body: <p>I've only been using Weka for a couple of weeks but I am absolutely blown away by how great it is!</p>
  
  <p>But I have a question, I have a dataset with a target column which is either True or False. </p>
  
  <p>6709 instances in my dataset are True</p>
  
  <p>25318 instances are False.</p>
  
  <p>I want to randomly add duplicates of my True instances to produce a new dataset with 25318 True and 25318 False.</p>
  
  <p>The only filter I can find which does this is the supervised Resample filter however I am having trouble understanding what parameters I should use.</p>
  
  <p>(there might be a better filter to do what I want)</p>
  
  <p>I've got some success with these parameters </p>
  
  <pre><code>biasToUniformClass = 1.0
  invertSelection = False
  noReplacement = False
  randomSeed = 1
  sampleSizePercent = 157.5 (a magic number I've arrived at by trial and error)
  </code></pre>
  
  <p>This produces 25277 True and 25165 False. Not exactly what I want, but quite close.</p>
  
  <p>The problem is that I cant figure out how to arrive at the magic number. I'm also not getting exactly the numbers of instances that I really want.</p>
  
  <p>Is there a better filter for this purpose?
  If not, is there a way to calculate the sampleSizePercent magic number?</p>
  
  <p>Any help is greatly appreciated :)</p>
  
  <p>Supplemental question, am I best to run NominalToBinary on my boolean columns to ensure they are Binary? I'm using a NaiveBayes classifier (at the moment) and I don't have any missing instances.</p>
  
  <p>Jason</p>