diff --git a/trees/cart_classifier.go b/trees/cart_classifier.go
index 17a3ee7..fee9043 100644
--- a/trees/cart_classifier.go
+++ b/trees/cart_classifier.go
@@ -39,7 +39,7 @@ type CARTDecisionTreeClassifier struct {
 }
 
 // Calculate Gini Impurity of Target Labels
-func giniImpurity(y []int64, labels []int64) (float64, int64) {
+func computeGiniImpurityAndModeLabel(y []int64, labels []int64) (float64, int64) {
 	nInstances := len(y)
 	gini := 0.0
 	maxLabelCount := 0
@@ -62,7 +62,7 @@ func giniImpurity(y []int64, labels []int64) (float64, int64) {
 }
 
 // Calculate Entropy loss of Target Labels
-func entropy(y []int64, labels []int64) (float64, int64) {
+func computeEntropyAndModeLabel(y []int64, labels []int64) (float64, int64) {
 	nInstances := len(y)
 	entropy := 0.0
 	maxLabelCount := 0
@@ -91,9 +91,9 @@ func entropy(y []int64, labels []int64) (float64, int64) {
 
 func calculateClassificationLoss(y []int64, labels []int64, criterion string) (float64, int64) {
 	if criterion == GINI {
-		return giniImpurity(y, labels)
+		return computeGiniImpurityAndModeLabel(y, labels)
 	} else if criterion == ENTROPY {
-		return entropy(y, labels)
+		return computeEntropyAndModeLabel(y, labels)
 	} else {
 		panic("Invalid impurity function, choose from GINI or ENTROPY")
 	}
diff --git a/trees/cart_regressor.go b/trees/cart_regressor.go
index b94da1d..3509a15 100644
--- a/trees/cart_regressor.go
+++ b/trees/cart_regressor.go
@@ -59,7 +59,7 @@ func meanAbsoluteError(y []float64, yBar float64) float64 {
 }
 
 // Turn Mean Absolute Error into impurity function for decision trees.
-func maeImpurity(y []float64) (float64, float64) {
+func computeMaeImpurityAndAverage(y []float64) (float64, float64) {
 	yHat := average(y)
 	return meanAbsoluteError(y, yHat), yHat
 }
@@ -76,16 +76,16 @@ func meanSquaredError(y []float64, yBar float64) float64 {
 }
 
 // Convert mean squared error into impurity function for decision trees
-func mseImpurity(y []float64) (float64, float64) {
+func computeMseImpurityAndAverage(y []float64) (float64, float64) {
 	yHat := average(y)
 	return meanSquaredError(y, yHat), yHat
 }
 
 func calculateRegressionLoss(y []float64, criterion string) (float64, float64) {
 	if criterion == MAE {
-		return maeImpurity(y)
+		return computeMaeImpurityAndAverage(y)
 	} else if criterion == MSE {
-		return mseImpurity(y)
+		return computeMseImpurityAndAverage(y)
 	} else {
 		panic("Invalid impurity function, choose from MAE or MSE")
 	}