diff --git a/Cargo.toml b/Cargo.toml
index 4eb09fc..7f83962 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -15,7 +15,7 @@ categories = ["science"]
 [dependencies]
 smartcore = { path = "../smartcore", default-features = false, features=["serde", "nalgebra-bindings", "ndarray-bindings", "datasets"]}
 structopt = "0.3.17"
-ndarray = "0.14"
+ndarray = "0.15"
 nalgebra = "0.31"
 plotters = "^0.3.0"
 serde = "1.0.115"
diff --git a/src/model_selection.rs b/src/model_selection.rs
index 176ff1d..7de89db 100644
--- a/src/model_selection.rs
+++ b/src/model_selection.rs
@@ -73,8 +73,8 @@ pub fn lr_cross_validate() {
         LogisticRegression::fit,
         &x,
         &y,
-        Default::default(),
-        KFold::default().with_n_splits(3),
+        &Default::default(),
+        &KFold::default().with_n_splits(3),
         accuracy,
     )
     .unwrap();
diff --git a/src/supervised.rs b/src/supervised.rs
index c5d0590..fdfe63b 100644
--- a/src/supervised.rs
+++ b/src/supervised.rs
@@ -39,7 +39,7 @@ pub fn diabetes() {
     let y = diabetes_data.target;
 
     // Split dataset into training/test (80%/20%)
-    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true);
+    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true, None);
 
     // SVM
     let y_hat_svm = SVR::fit(
@@ -72,7 +72,7 @@ pub fn breast_cancer() {
     let y = cancer_data.target;
 
     // Split dataset into training/test (80%/20%)
-    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true);
+    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true, None);
 
     // KNN classifier
     let y_hat_knn = KNNClassifier::fit(&x_train, &y_train, Default::default())
@@ -122,7 +122,7 @@ pub fn boston() {
     // These are our target values
     let y = boston_data.target;
 
-    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true);
+    let (x_train, x_test, y_train, y_test) = train_test_split(&x, &y, 0.2, true, None);
 
     // KNN regressor
     let y_hat_knn = KNNRegressor::fit(&x_train, &y_train, Default::default())