title: R random forest by sensitivity
body: <p>Is it possible to run a supervised classification random forest maximizing sensitivity (TP/(TP+FN))?
  As far as I know, Accuracy or Kappa are the metric.
  Below, an real example where both Kappa and Accuracy miss to evaluate the model as desired. As pointed in the answer and comments (@Hanjo and @Aaron), sensitivity alone is not a good metric. </p>
  
  <pre><code>      0    1     T  
  0  1213   50  1263  
  1   608   63   671  
  T  1821  113  1934  
  
  &gt; Precisao(prev_table)
  [1] "accuracy(TP+TN/T)= 0.66"
  [1] "precision(TP/TP+FP)= 0.558"
  [1] "sensitivity(TP/TP+FN)= 0.0939"
  [1] "positive= 671 0.347"
  [1] "negative= 1263 0.653"
  [1] "predicted positive= 113 0.0584"
  [1] "predicted negative= 1821 0.942"
  [1] "Total= 1934"
  </code></pre>
  
  <p>This real x predicted results are poor to the goal.</p>