title: How to optimize precision-recall curve instead of AUC-ROC curve in python scikit-learn?
body: <p>I am asking a follow-up question as suggested from my previous post - <a href="https://stackoverflow.com/questions/33294574/good-roc-curve-but-poor-precision-recall-curve?rq=1">Good ROC curve but poor precision-recall curve</a>. I am only using the default setting with Python scikit-learn. It seems like the optimization is on AUC-ROC, but I am more interested in optimizing precision-recall. The following is my codes.</p>
  
  <pre><code># Get ROC 
  y_score = classifierUsed2.decision_function(X_test)
  false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_score)
  roc_auc = auc(false_positive_rate, true_positive_rate)
  print 'AUC-'+ethnicity_tar+'=',roc_auc
  # Plotting
  ax1.plot(false_positive_rate, true_positive_rate, c=color, label=('AUC-'+ethnicity_tar+'= %0.2f'%roc_auc))
  ax1.plot([0,1],[0,1], color='lightgrey', linestyle='--')
  ax1.legend(loc='lower right', prop={'size':8})
  
  # Get P-R pairs
  precision, recall, prThreshold = precision_recall_curve(y_test, y_score)
  # Plotting
  ax2.plot(recall, precision, c=color, label=ethnicity_tar)
  ax2.legend(loc='upper right', prop={'size':8})
  </code></pre>
  
  <p>Where and how do I insert python codes to change the setting so I can optimize the precision-recall?</p>