diff --git a/src/dataset.rs b/src/dataset.rs
index fab58d1..3add5de 100644
--- a/src/dataset.rs
+++ b/src/dataset.rs
@@ -1,13 +1,13 @@
-use pyo3::prelude::*;
-use rand::{rngs::StdRng, Rng};
-use std::fs;
-use arrow::array::{Float32Array};
-use arrow::record_batch::RecordBatch;
+use arrow::array::Float32Array;
 use arrow::compute::cast;
 use arrow::csv;
-use std::fs::File;
 use arrow::datatypes::DataType;
 use arrow::pyarrow::PyArrowConvert;
+use arrow::record_batch::RecordBatch;
+use pyo3::prelude::*;
+use rand::{rngs::StdRng, Rng};
+use std::fs;
+use std::fs::File;
 
 use pyo3::types::PyAny;
 
@@ -22,10 +22,9 @@ pub struct Dataset {
 }
 
 impl Dataset {
-
     fn _from_pyarrow(df: &PyAny) -> Dataset {
         let batch = RecordBatch::from_pyarrow(df).unwrap();
-        
+
         let feature_names = batch
             .schema()
             .fields()
@@ -41,22 +40,22 @@ impl Dataset {
             feature_matrix: feature_matrix[0..feature_matrix.len() - 1].to_vec(),
             target_name: feature_names.last().unwrap().to_string(),
             target_vector: feature_matrix.last().unwrap().to_vec(),
-        }       
+        }
     }
 
-    fn _read_batch(batch: RecordBatch) -> Vec<Vec<f32>>{
+    fn _read_batch(batch: RecordBatch) -> Vec<Vec<f32>> {
         batch
-        .columns()
-        .iter()
-        .map(|c| cast(c, &DataType::Float32).unwrap())
-        .map(|c| {
-            c.as_any()
-                .downcast_ref::<Float32Array>()
-                .unwrap()
-                .values()
-                .to_vec()
-        })
-        .collect::<Vec<_>>()
+            .columns()
+            .iter()
+            .map(|c| cast(c, &DataType::Float32).unwrap())
+            .map(|c| {
+                c.as_any()
+                    .downcast_ref::<Float32Array>()
+                    .unwrap()
+                    .values()
+                    .to_vec()
+            })
+            .collect::<Vec<_>>()
     }
 
     fn _read_csv(path: &str, sep: &str) -> Dataset {
@@ -100,7 +99,7 @@ impl Dataset {
             feature_uniform: vec![false; self.feature_names.len()],
             feature_matrix: vec![],
             target_name: self.target_name.clone(),
-            target_vector: vec![]
+            target_vector: vec![],
         }
     }
 
@@ -179,7 +178,6 @@ mod tests {
             target_vector: vec![1.0, 0.0],
         };
 
-
         assert_eq!(expected, got);
     }
 
diff --git a/src/tests.rs b/src/tests.rs
index aa859de..9a7bc93 100644
--- a/src/tests.rs
+++ b/src/tests.rs
@@ -12,7 +12,7 @@ mod tests {
     fn test_integration() {
         let train = Dataset::read_csv("datasets/diabetes_train.csv", ",");
         let test = Dataset::read_csv("datasets/diabetes_test.csv", ",");
-        let dt = DecisionTree::train_reg(&train, Some(5), Some(1),  None, Some(42));
+        let dt = DecisionTree::train_reg(&train, Some(5), Some(1), None, Some(42));
         let mut pred = test.clone();
         dt.predict(&mut pred);
         assert_eq!(r2(&test.target_vector, &pred.target_vector) > 0.28, true);
@@ -30,7 +30,7 @@ mod tests {
     #[test]
     fn decision_tree_breast_cancer() {
         let (train, test) = read_train_test_dataset("breast_cancer");
-        let dt = DecisionTree::train_clf(&train, Some(5), Some(1), None,Some(42));
+        let dt = DecisionTree::train_clf(&train, Some(5), Some(1), None, Some(42));
         let pred = dt.predict(&test);
         println!("Accuracy: {}", accuracy(&test.target_vector, &pred));
         assert_greater_than(accuracy(&test.target_vector, &pred), 0.83);
@@ -39,7 +39,7 @@ mod tests {
     #[test]
     fn decision_tree_housing() {
         let (train, test) = read_train_test_dataset("housing");
-        let dt = DecisionTree::train_reg(&train, Some(5), Some(1),None, Some(42));
+        let dt = DecisionTree::train_reg(&train, Some(5), Some(1), None, Some(42));
         let pred = dt.predict(&test);
         println!("R2: {}", r2(&test.target_vector, &pred));
         assert_greater_than(r2(&test.target_vector, &pred), 0.59);
@@ -48,7 +48,7 @@ mod tests {
     #[test]
     fn decision_tree_diabeties() {
         let (train, test) = read_train_test_dataset("diabetes");
-        let dt = DecisionTree::train_reg(&train, Some(5), Some(1), None,Some(42));
+        let dt = DecisionTree::train_reg(&train, Some(5), Some(1), None, Some(42));
         let pred = dt.predict(&test);
         println!("R2: {}", r2(&test.target_vector, &pred));
         assert_greater_than(r2(&test.target_vector, &pred), 0.30);
@@ -64,11 +64,10 @@ mod tests {
         (train, test)
     }
 
-
     #[test]
     fn random_forest_diabetes() {
         let (train, test) = read_train_test_dataset("diabetes");
-        let rf = RandomForest::train_reg(&train, 10, Some(5), Some(1), None,Some(42));
+        let rf = RandomForest::train_reg(&train, 10, Some(5), Some(1), None, Some(42));
         let pred = rf.predict(&test);
         println!("R2: {}", r2(&test.target_vector, &pred));
         assert_greater_than(r2(&test.target_vector, &pred), 0.38);
@@ -77,7 +76,7 @@ mod tests {
     #[test]
     fn random_forest_housing() {
         let (train, test) = read_train_test_dataset("housing");
-        let rf = RandomForest::train_reg(&train, 10, Some(5), Some(1), None,Some(42));
+        let rf = RandomForest::train_reg(&train, 10, Some(5), Some(1), None, Some(42));
         let pred = rf.predict(&test);
         println!("R2: {}", r2(&test.target_vector, &pred));
         assert_greater_than(r2(&test.target_vector, &pred), 0.641);
@@ -86,19 +85,18 @@ mod tests {
     #[test]
     fn random_forest_breast_cancer() {
         let (train, test) = read_train_test_dataset("breast_cancer");
-        let rf = RandomForest::train_clf(&train, 10, Some(5), Some(1), None,Some(42));
+        let rf = RandomForest::train_clf(&train, 10, Some(5), Some(1), None, Some(42));
         let pred = rf.predict(&test);
         let pred = classification_threshold(&pred, 0.5);
 
         println!("Accuracy: {}", accuracy(&test.target_vector, &pred),);
         assert_greater_than(accuracy(&test.target_vector, &pred), 0.96);
-
     }
 
     #[test]
     fn random_forest_breast_titanic() {
         let (train, test) = read_train_test_dataset("titanic");
-        let rf = RandomForest::train_clf(&train, 10, Some(5), Some(1), None,Some(42));
+        let rf = RandomForest::train_clf(&train, 10, Some(5), Some(1), None, Some(42));
         let pred = rf.predict(&test);
         let pred = classification_threshold(&pred, 0.5);
 
diff --git a/src/trees.rs b/src/trees.rs
index 5bcb5ce..a7a4480 100644
--- a/src/trees.rs
+++ b/src/trees.rs
@@ -55,9 +55,8 @@ impl RandomForest {
         let default_train_options = TrainOptions::default_options(train.feature_names.len() as i32);
         let params = TrainOptions {
             max_depth: max_depth.unwrap_or(default_train_options.max_depth),
-            min_samples_leaf: min_samples_leaf
-                .unwrap_or(default_train_options.min_samples_leaf),
-            max_features: max_features.unwrap_or(default_train_options.max_features)
+            min_samples_leaf: min_samples_leaf.unwrap_or(default_train_options.min_samples_leaf),
+            max_features: max_features.unwrap_or(default_train_options.max_features),
         };
 
         let trees: Vec<Tree> = (0..n_estimators)
@@ -90,9 +89,8 @@ impl RandomForest {
         let default_train_options = TrainOptions::default_options(train.feature_names.len() as i32);
         let params = TrainOptions {
             max_depth: max_depth.unwrap_or(default_train_options.max_depth),
-            min_samples_leaf: min_samples_leaf
-                .unwrap_or(default_train_options.min_samples_leaf),
-            max_features: max_features.unwrap_or(default_train_options.max_features)
+            min_samples_leaf: min_samples_leaf.unwrap_or(default_train_options.min_samples_leaf),
+            max_features: max_features.unwrap_or(default_train_options.max_features),
         };
         let trees: Vec<Tree> = (0..n_estimators)
             .into_par_iter()
@@ -137,8 +135,6 @@ impl RandomForest {
     }
 }
 
-
-
 #[pymethods]
 impl DecisionTree {
     #[staticmethod]
@@ -153,9 +149,8 @@ impl DecisionTree {
         let default_train_options = TrainOptions::default_options(train.feature_names.len() as i32);
         let params = TrainOptions {
             max_depth: max_depth.unwrap_or(default_train_options.max_depth),
-            min_samples_leaf: min_samples_leaf
-                .unwrap_or(default_train_options.min_samples_leaf),
-            max_features: max_features.unwrap_or(default_train_options.max_features)
+            min_samples_leaf: min_samples_leaf.unwrap_or(default_train_options.min_samples_leaf),
+            max_features: max_features.unwrap_or(default_train_options.max_features),
         };
 
         DecisionTree {
@@ -181,9 +176,8 @@ impl DecisionTree {
         let default_train_options = TrainOptions::default_options(train.feature_names.len() as i32);
         let params = TrainOptions {
             max_depth: max_depth.unwrap_or(default_train_options.max_depth),
-            min_samples_leaf: min_samples_leaf
-                .unwrap_or(default_train_options.min_samples_leaf),
-            max_features: max_features.unwrap_or(default_train_options.max_features)
+            min_samples_leaf: min_samples_leaf.unwrap_or(default_train_options.min_samples_leaf),
+            max_features: max_features.unwrap_or(default_train_options.max_features),
         };
         DecisionTree {
             tree: Tree::fit(&train, 0, params, gini_coefficient_split_feature, &mut rng),