MAPE metric: output number range changed, unit tests added

CHANGELOG.md

@@ -1,7 +1,7 @@
 # Changelog
 
 ## 15.1.0
-- Metrics: output range squeezed to [0, 1]
+- MAPE metric: output range squeezed to [0, 1]
 
 ## 15.0.1
 - RegressorAssessor: unit tests added

e2e/knn_regressor_test.dart

@@ -36,7 +36,7 @@ void main() {
         'metric, dtype=DType.float64', () async {
       final scores = await evaluateKnnRegressor(MetricType.mape, DType.float64);
 
-      expect(scores.mean(), lessThan(50));
+      expect(scores.mean(), lessThan(0.5));
     });
   });
 }

e2e/lasso_regressor.dart

@@ -33,15 +33,15 @@ void main() {
       final scores = await evaluateLassoRegressor(MetricType.mape,
           DType.float32);
 
-      expect(scores.mean(),  lessThan(50));
+      expect(scores.mean(),  lessThan(0.5));
     });
 
     test('should return adequate error on mape metric, '
         'dtype=DType.float64', () async {
       final scores = await evaluateLassoRegressor(MetricType.mape,
           DType.float64);
 
-      expect(scores.mean(),  lessThan(50));
+      expect(scores.mean(),  lessThan(0.5));
     });
   });
 }

e2e/linear_regressor_test.dart

@@ -35,15 +35,15 @@ void main() async {
         'dtype=DType.float32', () async {
       expect(
         (await evaluateLinearRegressor(MetricType.mape, DType.float32)).mean(),
-        lessThan(50),
+        lessThan(0.5),
       );
     });
 
     test('should return adequate error on mape metric, '
         'dtype=DType.float64', () async {
       expect(
         (await evaluateLinearRegressor(MetricType.mape, DType.float64)).mean(),
-        lessThan(50),
+        lessThan(0.5),
       );
     });
   });

lib/src/metric/metric_type.dart

@@ -1 +1,25 @@
-enum MetricType { mape, rmse, accuracy, precision }
+/// Metrics for measuring the quality of the prediction.
+enum MetricType {
+  /// Mean percentage absolute error (MAPE), a regression metric. The less the
+  /// score produced by the metric, the better the prediction's quality is. Can
+  /// lead to error if there are zero values among the original values. Normally,
+  /// the metric produces scores within the range [0, 1], but extremely high
+  /// predicted values (>> original values) can produce scores which are
+  /// greater than 1.
+  mape,
+
+  /// Root mean squared error, a regression metric. The less the score produced
+  /// by the metric, the better the prediction's quality is. The metric produces
+  /// scores within the range [0, +Infinity]
+  rmse,
+
+  /// A classification metric. The greater the score produced by the metric, the
+  /// better the prediction's quality is. The metric produces scores within the
+  /// range [0, 1]
+  accuracy,
+
+  /// A classification metric. The greater the score produced by the metric, the
+  /// better the prediction's quality is. The metric produces scores within the
+  /// range [0, 1]
+  precision,
+}