diff --git a/ensemble/randomforest.go b/ensemble/randomforest.go
index 3776ebe..a27cca0 100644
--- a/ensemble/randomforest.go
+++ b/ensemble/randomforest.go
@@ -29,15 +29,15 @@ func NewRandomForest(forestSize int, features int) RandomForest {
 }
 
 // Train builds the RandomForest on the specified instances
-func (f *RandomForest) Train(on *base.Instances) {
+func (f *RandomForest) Fit(on *base.Instances) {
 	f.Model = new(meta.BaggedModel)
 	for i := 0; i < f.ForestSize; i++ {
 		tree := new(trees.RandomTree)
-		tree.Rules = new(trees.RandomTreeRule)
-		tree.Attributes = f.Features
+		tree.Rule = new(trees.RandomTreeRuleGenerator)
+		tree.Rule.Attributes = f.Features
 		f.Model.AddModel(tree)
 	}
-	f.Model.Train(on)
+	f.Model.Fit(on)
 }
 
 // Predict generates predictions from a trained RandomForest
diff --git a/ensemble/randomforest_test.go b/ensemble/randomforest_test.go
index 5ecdd04..b8dac72 100644
--- a/ensemble/randomforest_test.go
+++ b/ensemble/randomforest_test.go
@@ -20,7 +20,7 @@ func TestRandomForest1(testEnv *testing.T) {
 	filt.Run(insts[1])
 	filt.Run(insts[0])
 	rf := NewRandomForest(10, 2)
-	rf.Train(insts[0])
+	rf.Fit(insts[0])
 	predictions := rf.Predict(insts[1])
 	fmt.Println(predictions)
 	confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
diff --git a/meta/bagging.go b/meta/bagging.go
index 5a1a5be..23cb6aa 100644
--- a/meta/bagging.go
+++ b/meta/bagging.go
@@ -15,10 +15,10 @@ type BaggedModel struct {
 }
 
 func (b *BaggedModel) generateTrainingInstances(from *base.Instances) *base.Instances {
-	from = from.SampleWithReplacement(from.Rows)
-	return from
+	return from.SampleWithReplacement(from.Rows)
 }
 
+// AddModel adds a base.Classifier to the current model
 func (b *BaggedModel) AddModel(m base.Classifier) {
 	b.Models = append(b.Models, m)
 }
diff --git a/trees/random.go b/trees/random.go
index 0ab9539..0c8c2bf 100644
--- a/trees/random.go
+++ b/trees/random.go
@@ -36,14 +36,14 @@ func (r *RandomTreeRuleGenerator) GenerateSplitAttribute(f *base.Instances) base
 type RandomTree struct {
 	base.BaseClassifier
 	Root *DecisionTreeNode
-	Rule RandomTreeRuleGenerator
+	Rule *RandomTreeRuleGenerator
 }
 
 func NewRandomTree(attrs int) *RandomTree {
 	return &RandomTree{
 		base.BaseClassifier{},
 		nil,
-		RandomTreeRuleGenerator{
+		&RandomTreeRuleGenerator{
 			attrs,
 			InformationGainRuleGenerator{},
 		},
@@ -52,7 +52,7 @@ func NewRandomTree(attrs int) *RandomTree {
 
 // Train builds a RandomTree suitable for prediction
 func (rt *RandomTree) Fit(from *base.Instances) {
-	rt.Root = InferID3Tree(from, &rt.Rule)
+	rt.Root = InferID3Tree(from, rt.Rule)
 }
 
 // Predict returns a set of Instances containing predictions