diff --git a/samples/machine-learning/wine/Host.cs b/samples/machine-learning/wine/Host.cs
index d52d71feb8e1..fdb9f707e5ce 100644
--- a/samples/machine-learning/wine/Host.cs
+++ b/samples/machine-learning/wine/Host.cs
@@ -33,12 +33,13 @@ static async Task Main(string[] args)
    
             // After training, we can use the validation data to test the accuracy
             // of our new classifier.
-            var testMisses = await ValidateWineModel.Run(
+            var missRate = await ValidateWineModel.Run(
                 targetMachine,
                 optimizedParameters,
                 optimizedBias
             );
-            System.Console.WriteLine($"Observed {testMisses} misclassifications.");
+
+            System.Console.WriteLine($"Observed {100 * missRate:F2}% misclassifications.");
         }
     }
 
diff --git a/samples/machine-learning/wine/Training.qs b/samples/machine-learning/wine/Training.qs
index 6836711d6bc8..9378a5ed370a 100644
--- a/samples/machine-learning/wine/Training.qs
+++ b/samples/machine-learning/wine/Training.qs
@@ -2,6 +2,7 @@
 // Licensed under the MIT License.
 
 namespace Microsoft.Quantum.Samples {
+    open Microsoft.Quantum.Convert;
     open Microsoft.Quantum.Random;
     open Microsoft.Quantum.Intrinsic;
     open Microsoft.Quantum.Canon;
@@ -68,7 +69,7 @@ namespace Microsoft.Quantum.Samples {
     operation ValidateWineModel(
         parameters : Double[],
         bias : Double
-    ) : Int {
+    ) : Double {
         // Get the remaining samples to use as validation data.
         let samples = (Datasets.WineData())[143...];
         let tolerance = 0.005;
@@ -80,7 +81,7 @@ namespace Microsoft.Quantum.Samples {
             nMeasurements,
             DefaultSchedule(samples)
         );
-        return results::NMisclassifications;
+        return IntAsDouble(results::NMisclassifications) / IntAsDouble(Length(samples));
     }
 
 }