Cross validator entity: evaluate method now returns Future<Vector> in…

…stead of double

README.md

@@ -98,7 +98,7 @@ All are set, so we can do our classification.
 Evaluate our model via accuracy metric:
 
 ````dart
-final accuracy = validator.evaluate((samples, targetNames) => 
+final scores = await validator.evaluate((samples, targetNames) => 
     LogisticRegressor(
         samples,
         targetNames[0], // remember, we provided a list of just a single name
@@ -112,6 +112,13 @@ final accuracy = validator.evaluate((samples, targetNames) =>
     ), MetricType.accuracy);
 ````
 
+Since the CrossValidator's instance returns a Vector of scores as a result of our predictor evaluation, we may choose
+any way to reduce all the collected scores to a single number, for instance we may use Vector's `mean` method:
+
+```dart
+final accuracy = scores.mean();
+```  
+
 Let's print the score:
 ````dart
 print('accuracy on classification: ${accuracy.toStringAsFixed(2)}');
@@ -134,7 +141,7 @@ Future main() async {
   final samples = await fromCsv('datasets/pima_indians_diabetes_database.csv', headerExists: true);
   final targetColumnName = 'class variable (0 or 1)';
   final validator = CrossValidator.KFold(samples, [targetColumnName], numberOfFolds: 5);
-  final accuracy = validator.evaluate((samples, targetNames) => 
+  final scores = await validator.evaluate((samples, targetNames) => 
       LogisticRegressor(
           samples,
           targetNames[0], // remember, we provide a list of just a single name
@@ -146,6 +153,7 @@ Future main() async {
           interceptScale: .1,
           learningRateType: LearningRateType.constant
       ), MetricType.accuracy);
+  final accuracy = scores.mean();
 
   print('accuracy on classification: ${accuracy.toStringFixed(2)}');
 }
@@ -202,14 +210,15 @@ Let the `k` parameter be equal to `4`.
 Assess a knn regressor with the chosen `k` value using MAPE metric
 
 ````dart
-final error = validator.evaluate((samples, targetNames) => 
+final scores = await validator.evaluate((samples, targetNames) => 
   KnnRegressor(samples, targetNames[0], 4), MetricType.mape);
+final averageError = scores.mean();
 ````
 
 Let's print our error
 
 ````dart
-print('MAPE error on k-fold validation: ${error.toStringAsFixed(2)}%'); // it yields approx. 6.18
+print('MAPE error on k-fold validation: ${averageError.toStringAsFixed(2)}%'); // it yields approx. 6.18
 ````
 
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