diff --git a/src/ensemble/random_forest_classifier.rs b/src/ensemble/random_forest_classifier.rs
index e1d462a..011b0ba 100644
--- a/src/ensemble/random_forest_classifier.rs
+++ b/src/ensemble/random_forest_classifier.rs
@@ -137,13 +137,13 @@ impl<T: RealNumber> RandomForestClassifier<T> {
             yi[i] = classes.iter().position(|c| yc == *c).unwrap();
         }
 
-        let mtry = parameters.m.unwrap_or(
+        let mtry = parameters.m.unwrap_or_else(|| {
             (T::from(num_attributes).unwrap())
                 .sqrt()
                 .floor()
                 .to_usize()
-                .unwrap(),
-        );
+                .unwrap()
+        });
 
         let classes = y_m.unique();
         let k = classes.len();
diff --git a/src/lib.rs b/src/lib.rs
index 687becf..2142c8b 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -65,7 +65,6 @@
 //! ```
 
 #![allow(
-    clippy::or_fun_call,
     clippy::needless_range_loop,
     clippy::ptr_arg,
     clippy::len_without_is_empty,
diff --git a/src/metrics/cluster_hcv.rs b/src/metrics/cluster_hcv.rs
index bdefc8d..29a9db2 100644
--- a/src/metrics/cluster_hcv.rs
+++ b/src/metrics/cluster_hcv.rs
@@ -24,8 +24,8 @@ impl HCVScore {
         let contingency = contingency_matrix(&labels_true, &labels_pred);
         let mi: T = mutual_info_score(&contingency);
 
-        let homogeneity = entropy_c.map(|e| mi / e).unwrap_or(T::one());
-        let completeness = entropy_k.map(|e| mi / e).unwrap_or(T::one());
+        let homogeneity = entropy_c.map(|e| mi / e).unwrap_or_else(T::one);
+        let completeness = entropy_k.map(|e| mi / e).unwrap_or_else(T::one);
 
         let v_measure_score = if homogeneity + completeness == T::zero() {
             T::zero()
diff --git a/src/svm/svc.rs b/src/svm/svc.rs
index 62a9e01..f2d518b 100644
--- a/src/svm/svc.rs
+++ b/src/svm/svc.rs
@@ -561,7 +561,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
                     (
                         idx_1,
                         idx_2,
-                        k_v_12.unwrap_or(self.kernel.apply(&self.sv[idx_1].x, &self.sv[idx_2].x)),
+                        k_v_12.unwrap_or_else(|| {
+                            self.kernel.apply(&self.sv[idx_1].x, &self.sv[idx_2].x)
+                        }),
                     )
                 })
             }
@@ -597,7 +599,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
                     (
                         idx_1,
                         idx_2,
-                        k_v_12.unwrap_or(self.kernel.apply(&self.sv[idx_1].x, &self.sv[idx_2].x)),
+                        k_v_12.unwrap_or_else(|| {
+                            self.kernel.apply(&self.sv[idx_1].x, &self.sv[idx_2].x)
+                        }),
                     )
                 })
             }
diff --git a/src/tree/decision_tree_classifier.rs b/src/tree/decision_tree_classifier.rs
index 353c1bd..9fe1b1a 100644
--- a/src/tree/decision_tree_classifier.rs
+++ b/src/tree/decision_tree_classifier.rs
@@ -376,7 +376,8 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
                     let node = &self.nodes[node_id];
                     if node.true_child == None && node.false_child == None {
                         result = node.output;
-                    } else if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan())
+                    } else if x.get(row, node.split_feature)
+                        <= node.split_value.unwrap_or_else(T::nan)
                     {
                         queue.push_back(node.true_child.unwrap());
                     } else {
@@ -529,7 +530,7 @@ impl<T: RealNumber> DecisionTreeClassifier<T> {
         for i in 0..n {
             if visitor.samples[i] > 0 {
                 if visitor.x.get(i, self.nodes[visitor.node].split_feature)
-                    <= self.nodes[visitor.node].split_value.unwrap_or(T::nan())
+                    <= self.nodes[visitor.node].split_value.unwrap_or_else(T::nan)
                 {
                     true_samples[i] = visitor.samples[i];
                     tc += true_samples[i];
diff --git a/src/tree/decision_tree_regressor.rs b/src/tree/decision_tree_regressor.rs
index 39f3eb8..c30c9e2 100644
--- a/src/tree/decision_tree_regressor.rs
+++ b/src/tree/decision_tree_regressor.rs
@@ -282,7 +282,8 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
                     let node = &self.nodes[node_id];
                     if node.true_child == None && node.false_child == None {
                         result = node.output;
-                    } else if x.get(row, node.split_feature) <= node.split_value.unwrap_or(T::nan())
+                    } else if x.get(row, node.split_feature)
+                        <= node.split_value.unwrap_or_else(T::nan)
                     {
                         queue.push_back(node.true_child.unwrap());
                     } else {
@@ -401,7 +402,7 @@ impl<T: RealNumber> DecisionTreeRegressor<T> {
         for i in 0..n {
             if visitor.samples[i] > 0 {
                 if visitor.x.get(i, self.nodes[visitor.node].split_feature)
-                    <= self.nodes[visitor.node].split_value.unwrap_or(T::nan())
+                    <= self.nodes[visitor.node].split_value.unwrap_or_else(T::nan)
                 {
                     true_samples[i] = visitor.samples[i];
                     tc += true_samples[i];