In-memory & self-contained sample template.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs

@@ -20,18 +20,48 @@ public static void Example()
             var examples = GenerateRandomDataPoints(1000);
 
             // Convert the examples list to an IDataView object, which is consumable by ML.NET API.
-            var data = mlContext.Data.LoadFromEnumerable(examples);
+            var trainingData = mlContext.Data.LoadFromEnumerable(examples);
 
             // Define the trainer.
-            var pipeline = mlContext.BinaryClassification.Trainers.FastTree();
+            var pipeline = mlContext.Regression.Trainers.FastTree();
 
             // Train the model.
-            var model = pipeline.Fit(data);
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing examples. Use different random seed to make it different from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123));
+
+            // Run the model on test data set.
+            var transformedTestData = model.Transform(testData);
+
+            // Convert IDataView object to a list.
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedTestData, reuseRowObject: false).ToList();
+
+            // Look at 5 predictions
+            foreach (var p in predictions.Take(5))
+                Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
+
+            // Expected output:
+            //   Label: 0.985, Prediction: 0.938
+            //   Label: 0.155, Prediction: 0.131
+            //   Label: 0.515, Prediction: 0.517
+            //   Label: 0.566, Prediction: 0.519
+            //   Label: 0.096, Prediction: 0.089
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.Regression.Evaluate(transformedTestData);
+            SamplesUtils.ConsoleUtils.PrintMetrics(metrics);
+
+            // Expected output:
+            //   Mean Absolute Error: 0.05
+            //   Mean Squared Error: 0.00
+            //   Root Mean Squared Error: 0.06
+            //   RSquared: 0.95
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
         {
-            var random = new Random(0);
+            var random = new Random(seed);
             float randomFloat() => (float)random.NextDouble();
             for (int i = 0; i < count; i++)
             {
@@ -45,11 +75,21 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count)
             }
         }
 
+        // Example with label and 50 feature values. A data set is a collection of such examples.
         private class DataPoint
         {
             public float Label { get; set; }
             [VectorType(50)]
             public float[] Features { get; set; }
         }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public float Label { get; set; }
+            // Predicted score from the trainer.
+            public float Score { get; set; }
+        }
     }
 }