title: SVM classification with always high precision
body: <p>I have a binary classification problem and I'm trying to get precision-recall curve for my classifier. I use libsvm with RBF kernel and probability estimate option.</p>
  
  <p>To get the curve I'm changing decision threshold from 0 to 1 with steps of 0.1. But on every run, I get high precision even if recall decreases with increasing threshold. My false positive rate seems always low compared to true positives.</p>
  
  <p>My results are these:</p>
  
  <pre><code>Threshold: 0.1
  TOTAL TP:393, FP:1, FN: 49
  Precision:0.997462, Recall: 0.889140
  
  Threshold: 0.2
  TOTAL TP:393, FP:5, FN: 70
  Precision:0.987437, Recall: 0.848812
  
  Threshold: 0.3
  TOTAL TP:354, FP:4, FN: 78
  Precision:0.988827, Recall: 0.819444
  
  Threshold: 0.4
  TOTAL TP:377, FP:9, FN: 104
  Precision:0.976684, Recall: 0.783784
  
  Threshold: 0.5
  TOTAL TP:377, FP:5, FN: 120
  Precision:0.986911, Recall: 0.758551
  
  Threshold: 0.6
  TOTAL TP:340, FP:4, FN: 144
  Precision:0.988372, Recall: 0.702479
  
  Threshold: 0.7
  TOTAL TP:316, FP:5, FN: 166
  Precision:0.984424, Recall: 0.655602
  
  Threshold: 0.8
  TOTAL TP:253, FP:2, FN: 227
  Precision:0.992157, Recall: 0.527083
  
  Threshold: 0.9
  TOTAL TP:167, FP:2, FN: 354
  Precision:0.988166, Recall: 0.320537
  </code></pre>
  
  <p>Does this mean I have a good classifier or I have a fundamental mistake somewhere?</p>