diff --git a/src/ensemble/random_forest.rs b/src/ensemble/random_forest.rs
index f26acab..b318597 100644
--- a/src/ensemble/random_forest.rs
+++ b/src/ensemble/random_forest.rs
@@ -3,7 +3,7 @@ extern crate rand;
 use rand::Rng;
 use std::default::Default;
 use crate::linalg::Matrix;
-use crate::tree::decision_tree::{DecisionTree, DecisionTreeParameters, SplitCriterion, which_max};
+use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTreeClassifierParameters, SplitCriterion, which_max};
 
 #[derive(Debug, Clone)]
 pub struct RandomForestParameters {  
@@ -17,7 +17,7 @@ pub struct RandomForestParameters {
 #[derive(Debug)]
 pub struct RandomForest {    
     parameters: RandomForestParameters,
-    trees: Vec<DecisionTree>,
+    trees: Vec<DecisionTreeClassifier>,
     classes: Vec<f64>
 }
 
@@ -51,16 +51,16 @@ impl RandomForest {
 
         let classes = y_m.unique();        
         let k = classes.len(); 
-        let mut trees: Vec<DecisionTree> = Vec::new();
+        let mut trees: Vec<DecisionTreeClassifier> = Vec::new();
 
         for _ in 0..parameters.n_trees {
             let samples = RandomForest::sample_with_replacement(&yi, k);
-            let params = DecisionTreeParameters{
+            let params = DecisionTreeClassifierParameters{
                 criterion: parameters.criterion.clone(),
                 max_depth: parameters.max_depth,
                 min_samples_leaf: parameters.min_samples_leaf                
             };
-            let tree = DecisionTree::fit_weak_learner(x, y, samples, mtry, params);
+            let tree = DecisionTreeClassifier::fit_weak_learner(x, y, samples, mtry, params);
             trees.push(tree);
         }
 
diff --git a/src/tree/decision_tree.rs b/src/tree/decision_tree_classifier.rs
similarity index 93%
rename from src/tree/decision_tree.rs
rename to src/tree/decision_tree_classifier.rs
index 80f9a4a..5c1e483 100644
--- a/src/tree/decision_tree.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -4,16 +4,16 @@ use crate::linalg::Matrix;
 use crate::algorithm::sort::quick_sort::QuickArgSort;
 
 #[derive(Debug)]
-pub struct DecisionTreeParameters {           
+pub struct DecisionTreeClassifierParameters {           
     pub criterion: SplitCriterion,
     pub max_depth: Option<u16>,
     pub min_samples_leaf: u16    
 }
 
 #[derive(Debug)]
-pub struct DecisionTree {    
+pub struct DecisionTreeClassifier {    
     nodes: Vec<Node>,    
-    parameters: DecisionTreeParameters,    
+    parameters: DecisionTreeClassifierParameters,    
     num_classes: usize,
     classes: Vec<f64>,
     depth: u16
@@ -38,9 +38,9 @@ pub struct Node {
 }
 
 
-impl Default for DecisionTreeParameters {
+impl Default for DecisionTreeClassifierParameters {
     fn default() -> Self { 
-        DecisionTreeParameters {
+        DecisionTreeClassifierParameters {
             criterion: SplitCriterion::Gini,
             max_depth: None,
             min_samples_leaf: 1
@@ -139,15 +139,15 @@ pub(in crate) fn which_max(x: &Vec<u32>) -> usize {
     return which;
 }
 
-impl DecisionTree {
+impl DecisionTreeClassifier {
 
-    pub fn fit<M: Matrix>(x: &M, y: &M::RowVector, parameters: DecisionTreeParameters) -> DecisionTree {
+    pub fn fit<M: Matrix>(x: &M, y: &M::RowVector, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier {
         let (x_nrows, num_attributes) = x.shape();
         let samples = vec![1; x_nrows];
-        DecisionTree::fit_weak_learner(x, y, samples, num_attributes, parameters)
+        DecisionTreeClassifier::fit_weak_learner(x, y, samples, num_attributes, parameters)
     }
 
-    pub fn fit_weak_learner<M: Matrix>(x: &M, y: &M::RowVector, samples: Vec<u32>, mtry: usize, parameters: DecisionTreeParameters) -> DecisionTree {
+    pub fn fit_weak_learner<M: Matrix>(x: &M, y: &M::RowVector, samples: Vec<u32>, mtry: usize, parameters: DecisionTreeClassifierParameters) -> DecisionTreeClassifier {
         let y_m = M::from_row_vector(y.clone());
         let (_, y_ncols) = y_m.shape();
         let (_, num_attributes) = x.shape();
@@ -179,7 +179,7 @@ impl DecisionTree {
             order.push(x.get_col_as_vec(i).quick_argsort());
         }                        
 
-        let mut tree = DecisionTree{                                       
+        let mut tree = DecisionTreeClassifier{                                       
             nodes: nodes,            
             parameters: parameters,    
             num_classes: k,
@@ -435,9 +435,9 @@ mod tests {
             &[5.2, 2.7, 3.9, 1.4]]);
         let y = vec![0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.];
 
-        assert_eq!(y, DecisionTree::fit(&x, &y, Default::default()).predict(&x));
+        assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
         
-        assert_eq!(3, DecisionTree::fit(&x, &y, DecisionTreeParameters{criterion: SplitCriterion::Entropy, max_depth: Some(3), min_samples_leaf: 1}).depth);        
+        assert_eq!(3, DecisionTreeClassifier::fit(&x, &y, DecisionTreeClassifierParameters{criterion: SplitCriterion::Entropy, max_depth: Some(3), min_samples_leaf: 1}).depth);        
             
     }
 
@@ -467,7 +467,7 @@ mod tests {
             &[0.,0.,0.,1.]]);
         let y = vec![1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0., 1., 1., 0., 0., 0.];
 
-        assert_eq!(y, DecisionTree::fit(&x, &y, Default::default()).predict(&x));
+        assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
             
     }
 }
\ No newline at end of file
diff --git a/src/tree/mod.rs b/src/tree/mod.rs
index bea1d51..624c16a 100644
--- a/src/tree/mod.rs
+++ b/src/tree/mod.rs
@@ -1,2 +1,2 @@
 pub mod decision_tree_regressor;
-pub mod decision_tree;
\ No newline at end of file
+pub mod decision_tree_classifier;
\ No newline at end of file