Revise metrics definition (#11667)

* Revise metrics definition

* Fixed xrefs

* Revised based on feedback

Author: JRAlexander

docs/machine-learning/tutorials/movie-recommmendation.md

@@ -160,7 +160,7 @@ Initialize your data path variables, load the data from the *.csv files, and ret
 
 [!code-csharp[LoadData](~/samples/machine-learning/tutorials/MovieRecommendation/Program.cs#LoadData "Load data from data paths")]
 
-Data in ML.NET is represented as an [IDataView class](xref:Microsoft.Data.DataView.IDataView). `IDataView` is a flexible, efficient way of describing tabular data (numeric and text). Data can be loaded from a text file or in real time (for example, SQL database or log files) to an `IDataView` object.
+Data in ML.NET is represented as an [IDataView class](xref:Microsoft.ML.IDataView). `IDataView` is a flexible, efficient way of describing tabular data (numeric and text). Data can be loaded from a text file or in real time (for example, SQL database or log files) to an `IDataView` object.
 
 The [LoadFromTextFile()](xref:Microsoft.ML.TextLoaderSaverCatalog.LoadFromTextFile%60%601%28Microsoft.ML.DataOperationsCatalog,System.String,System.Char,System.Boolean,System.Boolean,System.Boolean,System.Boolean%29) defines the data schema and reads in the file. It takes in the data path variables and returns an `IDataView`. In this case, you provide the path for your `Test` and `Train` files and indicate both the text file header (so it can use the column names properly) and the comma character data separator (the default separator is a tab).
 
@@ -230,7 +230,7 @@ Fit the model to the `Train` data and return the trained model by adding the fol
 
 [!code-csharp[FitModel](~/samples/machine-learning/tutorials/MovieRecommendation/Program.cs#FitModel "Call the Fit method and return back the trained model")]
 
-The [Fit()](xref:Microsoft.ML.Trainers.MatrixFactorizationTrainer.Fit%28Microsoft.Data.DataView.IDataView,Microsoft.Data.DataView.IDataView%29) method trains your model with the provided training dataset. Technically, it executes the `Estimator` definitions by transforming the data and applying the training, and it returns back the trained model, which is a `Transformer`.
+The [Fit()](xref:Microsoft.ML.Trainers.MatrixFactorizationTrainer.Fit%28Microsoft.ML.IDataView,Microsoft.ML.IDataView%29) method trains your model with the provided training dataset. Technically, it executes the `Estimator` definitions by transforming the data and applying the training, and it returns back the trained model, which is a `Transformer`.
 
 Add the following as the next line of code in the `Main()` method to call your `BuildAndTrainModel()` method and return the trained model:
 
@@ -300,9 +300,11 @@ RSquared: 0.412556298844873
 
 In this output, there are 20 iterations. In each iteration, the measure of error decreases and converges closer and closer to 0.
 
-The `root of mean squared error` (RMS or RMSE) is frequently used to measure the differences between values predicted by a model and the values observed in a test dataset. Technically it's the square root of the average of the squares of the errors. You want your RMSE score to be as close to 1 as possible.
+The `root of mean squared error` (RMS or RMSE) is used to measure the differences between the model predicted values and the test dataset observed values. Technically it's the square root of the average of the squares of the errors. The lower it is, the better the model is.
 
-`R Squared` is the variation percentage in the predicted values explained by your model. It's a value between 0 and 1, and the closer the value is to 0, the better the model is.
+`R Squared` indicates how well data fits a model. Ranges from 0 to 1. A value of 0 means that the data is random or otherwise can't be fit to the model. A value of 1 means that the model exactly matches the data. You want your `R Squared` score to be as close to 1 as possible.
+
+Building successful models is an iterative process. This model has initial lower quality as the tutorial uses small datasets to provide quick model training. If you aren't satisfied with the model quality, you can try to improve it by providing larger training datasets or by choosing different training algorithms with different hyper-parameters for each algorithm. For more information, check out the [Improve your model](#improve-your-model) section below.
 
 ## Use your model