diff --git a/h2o-scala/src/main/scala/water/api/dsl/DslLegos.scala b/h2o-scala/src/main/scala/water/api/dsl/DslLegos.scala
index 82eb0886f2..66da04888f 100644
--- a/h2o-scala/src/main/scala/water/api/dsl/DslLegos.scala
+++ b/h2o-scala/src/main/scala/water/api/dsl/DslLegos.scala
@@ -281,15 +281,11 @@ trait T_H2O_Env[K<:HexKey, VT <: DFrame] { // Operating with only given represen
   def shutdown() = H2O.CLOUD.shutdown()
   
   // DRF API call
-  def drf(f: VT, r: VT, ntrees: Int = 50, classification:Boolean = false): DRF.DRFModel = {
+  def drf(ftrain: VT, ftest:VT, x:Seq[Int], y:Int, params: (DRF)=>DRF ): DRF.DRFModel = {
     val drf:DRF = new DRF()
-    val response = r.frame().vecs()(0)
-    response.rollupStats()
-    drf.source = new Frame(f.frame().names() ++ Array("response"), f.frame.vecs()++Array(response))
-    drf.response = response
-    drf.classification = classification
-    drf.ntrees = ntrees;
-    drf.invoke()
+    drf.source = ftrain(x++Seq(y)).frame()
+    drf.response = ftrain.frame().vec(y)
+    params(drf).invoke()
     return UKV.get(drf.dest())
   }
   
diff --git a/h2o-scala/src/main/scala/water/api/dsl/examples/Examples.scala b/h2o-scala/src/main/scala/water/api/dsl/examples/Examples.scala
index 42a1a6be49..c038e09f9b 100644
--- a/h2o-scala/src/main/scala/water/api/dsl/examples/Examples.scala
+++ b/h2o-scala/src/main/scala/water/api/dsl/examples/Examples.scala
@@ -6,6 +6,7 @@ import hex.deeplearning.DeepLearning
 import java.io.File
 import java.util.Random
 import hex.deeplearning.DeepLearning.ClassSamplingMethod
+import hex.drf.DRF
 
 /**
  * Simple example project.
@@ -41,21 +42,26 @@ object Examples {
   
   /** Call DRF, make a model, predict on a train data, compute MSE. */ 
   def example2() = {
-    banner(2, "Call DRF API and make a forest for cars dataset")
+    banner(2, """The example calls DRF API and produces a forest of regression trees for cars dataset.
+The response column represents number of cylinders for each car included in train data. Example then
+makes a prediction over train data and compute MSE of prediction."""")
+
     import water.api.dsl.H2ODsl._
     val f = parse(ffind("smalldata/cars.csv"))
-    val source = f(1) ++ f(3 to 7)
-    val response = f(2)
-    
+    val params = (p:DRF) => { import p._
+      ntrees = 10
+      classification = false
+      p
+    }
     // build a model
-    val model = drf(source, response, 10, false)  // doing regression
+    val model = drf(f, null, 3 to 7, 2, params)  // doing regression
     println("The DRF model is: \n" + model)
     // make a prediction
-    val predict:DFrame = model.score(source.frame())
+    val predict:DFrame = model.score(f.frame())
     
     println("Prediction on train data: \n" + predict)
     
-    // compute squared errors
+    // compute mean squared errors
     val serr = (response - predict)^2
     println("Errors per row: " + serr)
     // make a sum
@@ -123,10 +129,10 @@ object Examples {
   def userMain(args: Array[String]):Unit = {
     H2O.main(args)
     try {
-      example1()
+      //example1()
       example2()
-      example3()
-      example4()
+      //example3()
+      //example4()
     } catch {
       case t:Throwable => t.printStackTrace() // Simple debug
     } finally {
@@ -136,7 +142,7 @@ object Examples {
 
   // Print simple banner
   private def banner(id:Int, desc: String) = {
-    println("\n==== Example #"+id+" ====\n * "+desc )
+    println("\n==== Example #"+id+" ====\n "+desc )
     println(  "====================\n")
   }