title: Predicting counts using mlr
body: <p>I am using the learner <code>regr.gbm</code> to predict counts. Outside of <code>mlr</code>, using the <code>gbm</code> package directly, I use <code>distribution = "poisson"</code> and  <code>predict.gbm</code>, using <code>type = "response"</code>, returns predictions on the original scale, however I note that when I do this using <code>mlr</code>, the predictions appear to be on the log scale:</p>
  
  <pre><code>     truth    response
  913      4  0.67348708
  914      1  0.28413256
  915      3  0.41871237
  916      1  0.13027792
  2101     1 -0.02092168
  2102     2  0.23394970
  </code></pre>
  
  <p>However, the "truth" is not on the log scale and so I am concerned that the hyper-parameter tuning routines in <code>mlr</code> will not work. For comparison, this is the output I get with <code>distribution = "gaussian"</code>.</p>
  
  <pre><code>     truth response
  913      4 2.028177
  914      1 1.334658
  915      3 1.552846
  916      1 1.153072
  2101     1 1.006362
  2102     2 1.281811
  </code></pre>
  
  <p>What is the best way to handle this ?</p>