diff --git a/src/SharpLearning.Benchmarks/Benchmarks.ClassificationLearners.cs b/src/SharpLearning.Benchmarks/Benchmarks.ClassificationLearners.cs
index 4f6c883f..a433c55c 100644
--- a/src/SharpLearning.Benchmarks/Benchmarks.ClassificationLearners.cs
+++ b/src/SharpLearning.Benchmarks/Benchmarks.ClassificationLearners.cs
@@ -2,6 +2,7 @@
 using SharpLearning.AdaBoost.Learners;
 using SharpLearning.Containers.Matrices;
 using SharpLearning.DecisionTrees.Learners;
+using SharpLearning.GradientBoost.Learners;
 using SharpLearning.RandomForest.Learners;
 
 namespace SharpLearning.Benchmarks;
@@ -23,6 +24,7 @@ public class ClassificationLearners
         readonly ClassificationAdaBoostLearner m_classificationAdaBoostLearner = new();
         readonly ClassificationRandomForestLearner m_classificationRandomForestLearner = new();
         readonly ClassificationExtremelyRandomizedTreesLearner m_classificationExtremelyRandomizedTreesLearner = new();
+        readonly ClassificationBinomialGradientBoostLearner m_classificationBinomialGradientBoostLearner = new();
 
         [GlobalSetup]
         public void GlobalSetup()
@@ -57,5 +59,11 @@ public void ClassificationExtremelyRandomizedTreesLearner_Learn()
         {
             m_classificationExtremelyRandomizedTreesLearner.Learn(m_features, m_targets);
         }
+
+        [Benchmark]
+        public void ClassificationBinomialGradientBoostLearner_Learn()
+        {
+            m_classificationBinomialGradientBoostLearner.Learn(m_features, m_targets);
+        }
     }
 }
diff --git a/src/SharpLearning.Benchmarks/Benchmarks.Regression.cs b/src/SharpLearning.Benchmarks/Benchmarks.Regression.cs
deleted file mode 100644
index d8606575..00000000
--- a/src/SharpLearning.Benchmarks/Benchmarks.Regression.cs
+++ /dev/null
@@ -1,27 +0,0 @@
-using SharpLearning.DecisionTrees.Learners;
-using SharpLearning.AdaBoost.Learners;
-using SharpLearning.RandomForest.Learners;
-using SharpLearning.GradientBoost.Learners;
-
-namespace SharpLearning.Benchmarks;
-
-public static partial class Benchmarks
-{
-    public static partial class Regression
-    {
-        public class DecisionTreeLearner()
-            : RegressionLearnerBenchmark(() => new RegressionDecisionTreeLearner());
-
-        public class AdaboostLearner()
-            : RegressionLearnerBenchmark(() => new RegressionAdaBoostLearner());
-
-        public class RandomForestLearner()
-            : RegressionLearnerBenchmark(() => new RegressionRandomForestLearner());
-
-        public class ExtremelyRandomizedTreeLearner()
-            : RegressionLearnerBenchmark(() => new RegressionExtremelyRandomizedTreesLearner());
-
-        public class SquareLossGradientBoostLearner()
-            : RegressionLearnerBenchmark(() => new RegressionSquareLossGradientBoostLearner());
-    }
-}
diff --git a/src/SharpLearning.Benchmarks/RegressionLearnerBenchmark.cs b/src/SharpLearning.Benchmarks/RegressionLearnerBenchmark.cs
deleted file mode 100644
index e50474ab..00000000
--- a/src/SharpLearning.Benchmarks/RegressionLearnerBenchmark.cs
+++ /dev/null
@@ -1,38 +0,0 @@
-using System;
-using BenchmarkDotNet.Attributes;
-using SharpLearning.Common.Interfaces;
-using SharpLearning.Containers.Matrices;
-
-namespace SharpLearning.Benchmarks;
-
-[MemoryDiagnoser]
-public abstract class RegressionLearnerBenchmark(
-    Func<ILearner<double>> createLearner)
-{
-    readonly Func<ILearner<double>> m_createLearner = createLearner ?? throw new ArgumentNullException(nameof(createLearner));
-
-    const int Rows = 1000;
-    const int Cols = 10;
-    F64Matrix m_features;
-    double[] m_targets;
-
-    ILearner<double> m_learner;
-
-    [GlobalSetup]
-    public void GlobalSetup()
-    {
-        var seed = 42;
-        m_targets = DataGenerator.GenerateDoubles(Rows, 1, seed);
-        var features = DataGenerator.GenerateDoubles(Rows, Cols, seed);
-        m_features = new F64Matrix(features, Rows, Cols);
-
-        // Use default parameters.
-        m_learner = m_createLearner();
-    }
-
-    [Benchmark]
-    public void Learn()
-    {
-        m_learner.Learn(m_features, m_targets);
-    }
-}