Fix LogLossReduction (RIG) example values (#4098)

* Fix MulticlassClassificationMetrics.LogLossReduction example value

* Fix CalibratedBinaryClassificationMetrics.LogLossReduction example value

Author: SnakyBeaky

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

@@ -31,7 +31,7 @@ public sealed class CalibratedBinaryClassificationMetrics : BinaryClassification
         /// The log-loss reduction is scaled relative to a classifier that predicts the prior for every example:
         /// (LL(prior) - LL(classifier)) / LL(prior)
         /// This metric can be interpreted as the advantage of the classifier over a random prediction.
-        /// For example, if the RIG equals 20, it can be interpreted as "the probability of a correct prediction is
+        /// For example, if the RIG equals 0.2, it can be interpreted as "the probability of a correct prediction is
         /// 20% better than random guessing."
         /// </remarks>
         public double LogLossReduction { get; }
@@ -50,4 +50,4 @@ internal CalibratedBinaryClassificationMetrics(IHost host, DataViewRow overallRe
             Entropy = Fetch(BinaryClassifierEvaluator.Entropy);
         }
     }
-}
+}

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

@@ -33,7 +33,7 @@ public sealed class MulticlassClassificationMetrics
         /// The log-loss reduction is scaled relative to a classifier that predicts the prior for every example:
         /// (LL(prior) - LL(classifier)) / LL(prior)
         /// This metric can be interpreted as the advantage of the classifier over a random prediction.
-        /// For example, if the RIG equals 20, it can be interpreted as "the probability of a correct prediction is
+        /// For example, if the RIG equals 0.2, it can be interpreted as "the probability of a correct prediction is
         /// 20% better than random guessing".
         /// </remarks>
         public double LogLossReduction { get; private set; }
@@ -121,4 +121,4 @@ internal MulticlassClassificationMetrics(double accuracyMicro, double accuracyMa
             PerClassLogLoss = perClassLogLoss.ToImmutableArray();
         }
     }
-}
+}