diff --git a/src/ensemble/random_forest_regressor.rs b/src/ensemble/random_forest_regressor.rs
index 493b130..07ca0f2 100644
--- a/src/ensemble/random_forest_regressor.rs
+++ b/src/ensemble/random_forest_regressor.rs
@@ -208,10 +208,6 @@ mod tests {
             114.2, 115.7, 116.9,
         ];
 
-        let expected_y: Vec<f64> = vec![
-            85., 88., 88., 89., 97., 98., 99., 99., 102., 104., 109., 110., 113., 114., 115., 116.,
-        ];
-
         let y_hat = RandomForestRegressor::fit(
             &x,
             &y,
diff --git a/src/neighbors/knn_regressor.rs b/src/neighbors/knn_regressor.rs
index d8ddcf3..48dd564 100644
--- a/src/neighbors/knn_regressor.rs
+++ b/src/neighbors/knn_regressor.rs
@@ -143,7 +143,6 @@ impl<T: RealNumber, D: Distance<Vec<T>, T>> KNNRegressor<T, D> {
 
     fn predict_for_row(&self, x: Vec<T>) -> T {
         let search_result = self.knn_algorithm.find(&x, self.k);
-        println!("{:?}", search_result);
         let mut result = T::zero();
 
         let weights = self
@@ -196,7 +195,6 @@ mod tests {
         let y_exp = vec![2., 2., 3., 4., 4.];
         let knn = KNNRegressor::fit(&x, &y, Distances::euclidian(), Default::default());
         let y_hat = knn.predict(&x);
-        println!("{:?}", y_hat);
         assert_eq!(5, Vec::len(&y_hat));
         for i in 0..y_hat.len() {
             assert!((y_hat[i] - y_exp[i]).abs() < 1e-7);