Simplify F# demo code (dotnet#760)

* Simplify readme code

* Update Program.fs

Author: forki

samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/CreditCardFraudDetection/Program.fs

@@ -48,7 +48,7 @@ type TransactionFraudPrediction = {
     PredictedLabel: bool
     Score: float32
     Probability: float32
-    }
+}
 
 [<EntryPoint>]
 let main _ =
@@ -132,25 +132,17 @@ let main _ =
 
     let pipeline = 
         EstimatorChain()
-        |> fun x -> x.Append(mlContext.Transforms.Concatenate("Features", featureColumnNames))
-        |> fun x -> x.Append(mlContext.Transforms.DropColumns [|"Time"|])
-        |> fun x -> 
-            x.Append (
-                mlContext.Transforms.NormalizeMeanVariance (
-                    "FeaturesNormalizedByMeanVar", 
-                    "Features"
-                    )
-                )
-        |> fun x -> 
-            x.Append (
+          .Append(mlContext.Transforms.Concatenate("Features", featureColumnNames))
+          .Append(mlContext.Transforms.DropColumns [|"Time"|])
+          .Append(mlContext.Transforms.NormalizeMeanVariance("FeaturesNormalizedByMeanVar", "Features"))
+          .Append (
                 mlContext.BinaryClassification.Trainers.FastTree(
                     "Label", 
                     "FeaturesNormalizedByMeanVar", 
                     numberOfLeaves = 20, 
                     numberOfTrees = 100, 
                     minimumExampleCountPerLeaf = 10, 
-                    learningRate = 0.2
-                    )
+                    learningRate = 0.2)
                 )
 
     printfn "Training model"
@@ -179,7 +171,7 @@ let main _ =
 
     printfn "Making predictions"
     mlContext.Data.CreateEnumerable<TransactionObservation>(testData, reuseRowObject = false)
-    |> Seq.filter (fun x -> x.Label = true)
+    |> Seq.filter (fun x -> x.Label)
     // use 5 observations from the test data
     |> Seq.take 5
     |> Seq.iter (fun testData -> 

samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md

@@ -83,26 +83,17 @@ The initial code is similar to the following:
 
     let pipeline = 
         EstimatorChain()
-        |> fun x -> x.Append(mlContext.Transforms.Concatenate("Features", featureColumnNames))
-        |> fun x -> x.Append(mlContext.Transforms.DropColumns [|"Time"|])
-        |> fun x -> 
-            x.Append (
-                mlContext.Transforms.NormalizeMeanVariance (
-                    "FeaturesNormalizedByMeanVar", 
-                    "Features"
-                    )
-                )
-        |> fun x -> 
-            x.Append (
-                mlContext.BinaryClassification.Trainers.FastTree(
+          .Append(mlContext.Transforms.Concatenate("Features", featureColumnNames))
+          .Append(mlContext.Transforms.DropColumns [|"Time"|])
+          .Append(mlContext.Transforms.NormalizeMeanVariance("FeaturesNormalizedByMeanVar","Features"))
+          .Append(mlContext.BinaryClassification.Trainers.FastTree(
                     "Label", 
                     "FeaturesNormalizedByMeanVar", 
                     numberOfLeaves = 20, 
                     numberOfTrees = 100, 
                     minimumExampleCountPerLeaf = 10, 
-                    learningRate = 0.2
-                    )
-                )
+                    learningRate = 0.2)
+           )
 
 `````
 
@@ -130,7 +121,7 @@ After the model is trained, you can use the `Predict()` API to predict if a tran
 `````fsharp
     printfn "Making predictions"
     mlContext.Data.CreateEnumerable<TransactionObservation>(testData, reuseRowObject = false)
-    |> Seq.filter (fun x -> x.Label = true)
+    |> Seq.filter (fun x -> x.Label)
     // use 5 observations from the test data
     |> Seq.take 5
     |> Seq.iter (fun testData ->