title: One class learning to make predictions using MATLAB
body: <p>I am using MATLAB to build a prediction model which the target is binary.
  The problem is that those negative observations in my training data may indeed are positives but are just not detected. </p>
  
  <p>I started with a logistic regression model assuming the data is accurate and the results are less than satisfactory. After some research, I moved to one class learning hoping that I can focus on the only the part of data (the positives) that I am certain with.</p>
  
  <p>I looked up the related materials from MATLAB documentation and found that I can use <code>fitcsvm</code> to proceed.</p>
  
  <p>My current problem is:</p>
  
  <ol>
  <li>Am I on the right path? Can one class learning solve my problem?</li>
  <li>I tried to use <code>fitcsvm</code> to create a <code>ClassificationSVM</code> using all the positive observations that I have. </li>
  </ol>
  
  <p></p>
  
  <pre><code>model = fitcsvm(Instance,Label,'KernelScale','auto','Standardize',true)
  </code></pre>
  
  <p>However, when I try to use the model to predict</p>
  
  <pre><code>[label,score] = predict(model,Test)
  </code></pre>
  
  <p>All the labels predicted for my Test cases are 1. I think I did something wrong. So should I feed the svm only the positive observations that I have?
  If not what should I do?</p>