diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs
new file mode 100644
index 0000000000..86ab646083
--- /dev/null
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs
@@ -0,0 +1,98 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+
+namespace Samples.Dynamic
+{
+    public static class LogLossPerClass
+    {
+        public static void Example()
+        {
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            var mlContext = new MLContext(seed: 0);
+
+            // Create a list of training data points.
+            var dataPoints = GenerateRandomDataPoints(1000);
+
+            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
+
+            // Define the trainer.
+            var pipeline =
+                    // Convert the string labels into key types.
+                    mlContext.Transforms.Conversion.MapValueToKey(nameof(DataPoint.Label))
+                    // Apply a multiclass trainer.
+                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm());
+
+            // Train the model.
+            var model = pipeline.Fit(trainingData);
+
+            // Create testing data. 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);
+
+            // Evaluate the overall metrics
+            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+
+            // Find the original label values.
+            VBuffer<uint> keys = default;
+            transformedTestData.Schema["PredictedLabel"].GetKeyValues(ref keys);
+            var originalLabels = keys.DenseValues().ToArray();
+            for (var i = 0; i < originalLabels.Length; i++)
+                Console.WriteLine($"LogLoss for label {originalLabels[i]}: {metrics.PerClassLogLoss[i]:F4}");
+
+            // Expected output:
+            //   LogLoss for label 7: 0.2578
+            //   LogLoss for label 8: 0.2504
+            //   LogLoss for label 2: 0.1121
+            //   LogLoss for label 9: 0.2229
+            //   LogLoss for label 6: 0.1737
+            //   LogLoss for label 1: 0.2645
+            //   LogLoss for label 3: 0.2235
+            //   LogLoss for label 5: 0.1128
+            //   LogLoss for label 4: 0.1442
+        }
+
+        // Generates data points with random features and labels 1 to 9.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0)
+        {
+            var random = new Random(seed);
+            float randomFloat() => (float)(random.NextDouble() - 0.5);
+            for (int i = 0; i < count; i++)
+            {
+                // Generate Labels that are integers 1, 2 or 3
+                var label = random.Next(1, 10);
+                yield return new DataPoint
+                {
+                    Label = (uint)label,
+                    // Create random features that are correlated with the label.
+                    // The feature values are slightly increased by adding a constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                };
+            }
+        }
+
+        // Example with label and 20 feature values. A data set is a collection of such examples.
+        private class DataPoint
+        {
+            public uint Label { get; set; }
+            [VectorType(20)]
+            public float[] Features { get; set; }
+        }
+
+        // Class used to capture predictions.
+        private class Prediction
+        {
+            // Original label.
+            public uint Label { get; set; }
+            // Predicted label from the trainer.
+            public uint PredictedLabel { get; set; }
+        }
+    }
+}
diff --git a/src/Microsoft.ML.Data/Evaluators/Metrics/MulticlassClassificationMetrics.cs b/src/Microsoft.ML.Data/Evaluators/Metrics/MulticlassClassificationMetrics.cs
index b91638c8f6..acd2a95e7f 100644
--- a/src/Microsoft.ML.Data/Evaluators/Metrics/MulticlassClassificationMetrics.cs
+++ b/src/Microsoft.ML.Data/Evaluators/Metrics/MulticlassClassificationMetrics.cs
@@ -74,12 +74,17 @@ public sealed class MulticlassClassificationMetrics
         /// Gets the log-loss of the classifier for each class.
         /// </summary>
         /// <remarks>
-        /// The log-loss metric, is computed as follows:
-        /// LL = - (1/m) * sum( log(p[i]))
-        /// where m is the number of instances in the test set.
-        /// p[i] is the probability returned by the classifier if the instance belongs to the class,
+        /// The log-loss metric is computed as $-\frac{1}{m} \sum_{i=1}^m \log(p_i)$,
+        /// where $m$ is the number of instances in the test set.
+        /// $p_i$ is the probability returned by the classifier if the instance belongs to the class,
         /// and 1 minus the probability returned by the classifier if the instance does not belong to the class.
         /// </remarks>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[LogLoss](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs)]
+        /// ]]></format>
+        /// </example>
         public IReadOnlyList<double> PerClassLogLoss { get; }
 
         /// <summary>