Improve options conditionals

src/linear/logistic_regression.rs

@@ -518,12 +518,9 @@ impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y:
             for (i, y_hat_i) in y_hat.iterator(0).enumerate().take(n) {
                 result.set(
                     i,
-                    self.classes()[if RealNumber::sigmoid(*y_hat_i + intercept) > RealNumber::half()
+                    self.classes()[usize::from(
-                    {
+                        RealNumber::sigmoid(*y_hat_i + intercept) > RealNumber::half(),
-                        1
+                    )],
-                    } else {
-                        0
-                    }],
                 );
             }
         } else {

src/tree/decision_tree_classifier.rs

@@ -673,7 +673,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>>
         let mut is_pure = true;
         for i in 0..n_rows {
             if visitor.samples[i] > 0 {
-                if label == Option::None {
+                if label.is_none() {
                     label = Option::Some(visitor.y[i]);
                 } else if visitor.y[i] != label.unwrap() {
                     is_pure = false;

src/tree/decision_tree_regressor.rs

@@ -511,7 +511,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
             match queue.pop_front() {
                 Some(node_id) => {
                     let node = &self.nodes()[node_id];
-                    if node.true_child == None && node.false_child == None {
+                    if node.true_child.is_none() && node.false_child.is_none() {
                         result = node.output;
                     } else if x.get((row, node.split_feature)).to_f64().unwrap()
                         <= node.split_value.unwrap_or(std::f64::NAN)
@@ -557,7 +557,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
             self.find_best_split(visitor, n, sum, parent_gain, *variable);
         }
 
-        self.nodes()[visitor.node].split_score != Option::None
+        self.nodes()[visitor.node].split_score.is_some()
     }
 
     fn find_best_split(