diff --git a/src/ensemble/random_forest.rs b/src/ensemble/random_forest.rs
index b318597..09b47f6 100644
--- a/src/ensemble/random_forest.rs
+++ b/src/ensemble/random_forest.rs
@@ -9,7 +9,8 @@ use crate::tree::decision_tree_classifier::{DecisionTreeClassifier, DecisionTree
 pub struct RandomForestParameters {  
     pub criterion: SplitCriterion,   
     pub max_depth: Option<u16>,
-    pub min_samples_leaf: u16,      
+    pub min_samples_leaf: u16,    
+    pub min_samples_split: usize,          
     pub n_trees: u16,    
     pub mtry: Option<usize>
 }
@@ -27,6 +28,7 @@ impl Default for RandomForestParameters {
             criterion: SplitCriterion::Gini,
             max_depth: None,
             min_samples_leaf: 1,
+            min_samples_split: 2,
             n_trees: 100,
             mtry: Option::None
         }
@@ -58,7 +60,8 @@ impl RandomForest {
             let params = DecisionTreeClassifierParameters{
                 criterion: parameters.criterion.clone(),
                 max_depth: parameters.max_depth,
-                min_samples_leaf: parameters.min_samples_leaf                
+                min_samples_leaf: parameters.min_samples_leaf,   
+                min_samples_split: parameters.min_samples_split          
             };
             let tree = DecisionTreeClassifier::fit_weak_learner(x, y, samples, mtry, params);
             trees.push(tree);
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index 5c1e483..4ac70c2 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -7,7 +7,8 @@ use crate::algorithm::sort::quick_sort::QuickArgSort;
 pub struct DecisionTreeClassifierParameters {           
     pub criterion: SplitCriterion,
     pub max_depth: Option<u16>,
-    pub min_samples_leaf: u16    
+    pub min_samples_leaf: u16,
+    pub min_samples_split: usize
 }
 
 #[derive(Debug)]
@@ -43,7 +44,8 @@ impl Default for DecisionTreeClassifierParameters {
         DecisionTreeClassifierParameters {
             criterion: SplitCriterion::Gini,
             max_depth: None,
-            min_samples_leaf: 1
+            min_samples_leaf: 1,
+            min_samples_split: 2
         }
      }
 }
@@ -437,7 +439,7 @@ mod tests {
 
         assert_eq!(y, DecisionTreeClassifier::fit(&x, &y, Default::default()).predict(&x));
         
-        assert_eq!(3, DecisionTreeClassifier::fit(&x, &y, DecisionTreeClassifierParameters{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, min_samples_split: 2}).depth);        
             
     }
 
diff --git a/src/tree/decision_tree_regressor.rs b/src/tree/decision_tree_regressor.rs
index 4b5e8f2..752d201 100644
--- a/src/tree/decision_tree_regressor.rs
+++ b/src/tree/decision_tree_regressor.rs
@@ -33,7 +33,7 @@ impl Default for DecisionTreeRegressorParameters {
     fn default() -> Self { 
         DecisionTreeRegressorParameters {            
             max_depth: None,
-            min_samples_leaf: 2,
+            min_samples_leaf: 1,
             min_samples_split: 2
         }
      }
@@ -132,7 +132,7 @@ impl DecisionTreeRegressor {
 
         while tree.depth < tree.parameters.max_depth.unwrap_or(std::u16::MAX) {            
             match visitor_queue.pop_front() {
-                Some(node) => tree.split(node, mtry, &mut visitor_queue,),
+                Some(node) => tree.split(node, mtry, &mut visitor_queue),
                 None => break
             };     
         }        
@@ -186,7 +186,7 @@ impl DecisionTreeRegressor {
 
         let n: usize = visitor.samples.iter().sum();        
 
-        if n <= self.parameters.min_samples_split {
+        if n < self.parameters.min_samples_split {
             return false;
         }
 
@@ -289,13 +289,13 @@ impl DecisionTreeRegressor {
 
         let mut true_visitor = NodeVisitor::<M>::new(true_child_idx, true_samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);            
             
-        if tc > self.parameters.min_samples_split && self.find_best_cutoff(&mut true_visitor, mtry) {
+        if self.find_best_cutoff(&mut true_visitor, mtry) {
             visitor_queue.push_back(true_visitor);
         }
 
         let mut false_visitor = NodeVisitor::<M>::new(false_child_idx, visitor.samples, visitor.order, visitor.x, visitor.y, visitor.level + 1);
             
-        if fc > self.parameters.min_samples_split && self.find_best_cutoff(&mut false_visitor, mtry) {
+        if self.find_best_cutoff(&mut false_visitor, mtry) {
             visitor_queue.push_back(false_visitor);
         }
 
@@ -331,11 +331,10 @@ mod tests {
             &[ 554.894,  400.7,  282.7,  130.081, 1962.,   70.551]]);
         let y = vec![83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9];        
 
-        let expected_y = vec![85.6, 89.0, 85.6, 89.0, 97.15, 97.15, 100.0, 100.0, 100.0, 107.9, 107.9, 107.9, 113.4, 113.4, 116.3, 116.3];
-        let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default()).predict(&x);        
+        let y_hat = DecisionTreeRegressor::fit(&x, &y, Default::default()).predict(&x);              
 
         for i in 0..y_hat.len() {
-            assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
+            assert!((y_hat[i] - y[i]).abs() < 0.1);
         }        
 
         let expected_y = vec![87.3, 87.3, 87.3, 87.3, 98.9, 98.9, 98.9, 98.9, 98.9, 107.9, 107.9, 107.9, 114.85, 114.85, 114.85, 114.85];
@@ -343,7 +342,14 @@ mod tests {
 
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
-        }  
+        }
+        
+        let expected_y = vec![83.0, 88.35, 88.35, 89.5, 97.15, 97.15, 99.5, 99.5, 101.2, 104.6, 109.6, 109.6, 113.4, 113.4, 116.30, 116.30];
+        let y_hat = DecisionTreeRegressor::fit(&x, &y, DecisionTreeRegressorParameters{max_depth: Option::None, min_samples_leaf: 1, min_samples_split: 3}).predict(&x); 
+
+        for i in 0..y_hat.len() {
+            assert!((y_hat[i] - expected_y[i]).abs() < 0.1);
+        }
             
     }