Artidoro's comments

Author: sfilipi

src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs

@@ -1359,7 +1359,7 @@ public static string GetConfusionTableAsFormattedString(IHost host, IDataView co
             var confusionMatrix = GetConfusionMatrix(host, confusionDataView, binary, sample, false);
             var confusionTableString = GetConfusionTableAsString(confusionMatrix, false);
 
-            // if there is a Weight column, return the weighted confusionMatrix as well, from this function.
+            // If there is a Weight column, return the weighted confusionMatrix as well, from this function.
             if (isWeighted)
             {
                 confusionMatrix = GetConfusionMatrix(host, confusionDataView, binary, sample, true);

src/Microsoft.ML.Data/Evaluators/Metrics/ConfusionMatrix.cs

@@ -26,16 +26,17 @@ public sealed class ConfusionMatrix
         public IReadOnlyList<double> PerClassRecall { get; }
 
         /// <summary>
-        /// The confsion matrix counts for the combinations actual class/predicted class.
-        /// The actual classes are in the rows of the table, and the predicted classes in the columns.
+        /// The confusion matrix counts for the combinations actual class/predicted class.
+        /// The actual classes are in the rows of the table (stored in the outer <see cref="IReadOnlyList{T}"/>), and the predicted classes
+        ///  in the columns(stored in the inner <see cref="IReadOnlyList{T}"/>).
         /// </summary>
         public IReadOnlyList<IReadOnlyList<double>> Counts { get; }
 
         /// <summary>
         /// The indicators of the predicted classes.
         /// It might be the classes names, or just indices of the predicted classes, if the name mapping is missing.
         /// </summary>
-        public int NumberOfPredictedClasses { get; }
+        public int NumberOfClasses { get; }
 
         /// <summary>
         /// The indicators of the predicted classes.
@@ -83,10 +84,10 @@ internal ConfusionMatrix(IHost host, double[] precision, double[] recall, double
             IsBinary = isBinary;
             PredictedClassesIndicators = labelNames.AsReadOnly();
 
-            NumberOfPredictedClasses = confusionTableCounts.Length;
-            List<IReadOnlyList<double>> counts = new List<IReadOnlyList<double>>(NumberOfPredictedClasses);
+            NumberOfClasses = confusionTableCounts.Length;
+            List<IReadOnlyList<double>> counts = new List<IReadOnlyList<double>>(NumberOfClasses);
 
-            for (int i = 0; i < NumberOfPredictedClasses; i++)
+            for (int i = 0; i < NumberOfClasses; i++)
                 counts.Add(confusionTableCounts[i].ToList().AsReadOnly());
 
             Counts = counts.AsReadOnly();