title: RandomForest for Regression in R
body: <p>I'm experimenting with R and the randomForest Package, I have some experience with SVM and Neural Nets.
  My first test is to try and regress:  sin(x)+gaussian noise.
  With Neural Nets and svm I obtain a "relatively" nice approximation of sin(x) so the noise is filtered out and the learning algorithm doesn't overfit. (for decent parameters)
  When doing the same on randomForest I have a completely overfitted solution.
  I simply use (R 2.14.0, tried on 2.14.1 too, just in case):</p>
  
  <pre><code>library("randomForest")
  x&lt;-seq(-3.14,3.14,by=0.00628)
  noise&lt;-rnorm(1001)
  y&lt;-sin(x)+noise/4
  mat&lt;-matrix(c(x,y),ncol=2,dimnames=list(NULL,c("X","Y")))
  plot(x,predict(randomForest(Y~.,data=mat),mat),col="green")
  points(x,y)
  </code></pre>
  
  <p>I guess there is a magic option in randomForest to make it work correctly, I tried a few but I did not find the right lever to pull...</p>