diff --git a/src/csharp/Microsoft.Spark.E2ETest/IpcTests/ML/Feature/BinarizerTests.cs b/src/csharp/Microsoft.Spark.E2ETest/IpcTests/ML/Feature/BinarizerTests.cs
new file mode 100644
index 000000000..567674301
--- /dev/null
+++ b/src/csharp/Microsoft.Spark.E2ETest/IpcTests/ML/Feature/BinarizerTests.cs
@@ -0,0 +1,81 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.Spark.ML.Feature;
+using Microsoft.Spark.Sql;
+using Microsoft.Spark.Sql.Types;
+using Microsoft.Spark.UnitTest.TestUtils;
+using Xunit;
+
+namespace Microsoft.Spark.E2ETest.IpcTests.ML.Feature
+{
+    [Collection("Spark E2E Tests")]
+    public class BinarizerTests : FeatureBaseTests<Binarizer>
+    {
+        private readonly SparkSession _spark;
+
+        public BinarizerTests(SparkFixture fixture) : base(fixture)
+        {
+            _spark = fixture.Spark;
+        }
+
+        [Fact]
+        public void TestBinarizer()
+        {
+            string inputCol = "feature";
+            DataFrame input = _spark.CreateDataFrame(
+                new List<GenericRow>
+                {
+                    new GenericRow(new object[] {0, 0.1}),
+                    new GenericRow(new object[] {1, 0.8}),
+                    new GenericRow(new object[] {2, 0.2})
+                },
+                new StructType(new List<StructField>
+                {
+                    new StructField("id", new IntegerType()), new StructField(inputCol, new DoubleType())
+                }));
+            string expectedUid = "theUid";
+            string outputCol = "binarized_feature";
+            double threshold = 0.5;
+            Binarizer binarizer = new Binarizer(expectedUid)
+                .SetInputCol(inputCol)
+                .SetOutputCol(outputCol)
+                .SetThreshold(threshold);
+            DataFrame output = binarizer.Transform(input);
+            StructType outputSchema = binarizer.TransformSchema(input.Schema());
+
+            Assert.Contains(output.Schema().Fields, (f => f.Name == outputCol));
+            Assert.Contains(outputSchema.Fields, (f => f.Name == outputCol));
+            Assert.Equal(inputCol, binarizer.GetInputCol());
+            Assert.Equal(outputCol, binarizer.GetOutputCol());
+            Assert.Equal(threshold, binarizer.GetThreshold());
+
+            using (var tempDirectory = new TemporaryDirectory())
+            {
+                string savePath = Path.Join(tempDirectory.Path, "Binarizer");
+                binarizer.Save(savePath);
+
+                Binarizer loadedBinarizer = Binarizer.Load(savePath);
+                Assert.Equal(loadedBinarizer.Uid(), binarizer.Uid());
+            }
+
+            Assert.Equal(expectedUid, binarizer.Uid());
+        }
+
+        [Fact]
+        public void TestBinarizerWithArrayParams()
+        {
+            string[] inputCol = new[] {"col1", "col2"};
+            string[] outputCol = new[] {"feature1", "feature2"};
+            double[] threshold = new[] {0.5, 0.8};
+            Binarizer binarizer = new Binarizer()
+                .SetInputCols(inputCol)
+                .SetOutputCols(outputCol)
+                .SetThresholds(threshold);
+
+            Assert.Equal(inputCol, binarizer.GetInputCols());
+            Assert.Equal(outputCol, binarizer.GetOutputCols());
+            Assert.Equal(threshold, binarizer.GetThresholds());
+        }
+    }
+}
diff --git a/src/csharp/Microsoft.Spark/ML/Feature/Binarizer.cs b/src/csharp/Microsoft.Spark/ML/Feature/Binarizer.cs
new file mode 100644
index 000000000..d5888752d
--- /dev/null
+++ b/src/csharp/Microsoft.Spark/ML/Feature/Binarizer.cs
@@ -0,0 +1,161 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.Spark.Interop;
+using Microsoft.Spark.Interop.Ipc;
+using Microsoft.Spark.Sql;
+using Microsoft.Spark.Sql.Types;
+
+namespace Microsoft.Spark.ML.Feature
+{
+    /// <summary>
+    /// A <see cref="Binarizer"/>, Binarize a column of continuous features given a threshold.
+    /// </summary>
+    public class Binarizer : FeatureBase<Binarizer>, IJvmObjectReferenceProvider
+    {
+        private static readonly string s_binarizerClassName =
+            "org.apache.spark.ml.feature.Binarizer";
+
+        public Binarizer() : base(s_binarizerClassName)
+        {
+        }
+
+        public Binarizer(string uid) : base(s_binarizerClassName, uid)
+        {
+        }
+
+        internal Binarizer(JvmObjectReference jvmObject) : base(jvmObject)
+        {
+        }
+
+        JvmObjectReference IJvmObjectReferenceProvider.Reference => _jvmObject;
+        
+        /// <summary>
+        /// Gets the column that the <see cref="Binarizer"/> should read from
+        /// </summary>
+        /// <returns>string, input column</returns>
+        public string GetInputCol() => (string)(_jvmObject.Invoke("getInputCol"));
+
+        /// <summary>
+        /// Sets the column that the <see cref="Binarizer"/> should read from
+        /// </summary>
+        /// <param name="value">The name of the column to as the source</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetInputCol(string value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setInputCol", value));
+        
+        /// <summary>
+        /// Gets the columns that the <see cref="Binarizer"/> should read from
+        /// </summary>
+        /// <returns>array of strings, input column</returns>
+        public string[] GetInputCols() => (string[])(_jvmObject.Invoke("getInputCols"));
+
+        /// <summary>
+        /// Sets the columns that the <see cref="Binarizer"/> should read from
+        /// </summary>
+        /// <param name="value">The name of the columns to as the source</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetInputCols(string[] value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setInputCols", value));
+        
+        /// <summary>
+        /// Param for threshold used to <see cref="Binarizer"/> continuous features.
+        /// </summary>
+        /// <param name="value">Threshold value</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetThreshold(double value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setThreshold", value));
+        
+        /// <summary>
+        /// Gets threshold used to <see cref="Binarizer"/> continuous features.
+        /// </summary>
+        /// <returns>double, the threshold</returns>
+        public double GetThreshold() => (double)(_jvmObject.Invoke("getThreshold"));
+        
+        /// <summary>
+        /// Param for thresholds used to <see cref="Binarizer"/> continuous features.
+        /// </summary>
+        /// <param name="value">Threshold values</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetThresholds(double[] value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setThresholds", value));
+        
+        /// <summary>
+        /// Gets thresholds used to <see cref="Binarizer"/> continuous features.
+        /// </summary>
+        /// <returns>array of double, the thresholds</returns>
+        public double[] GetThresholds() => (double[])(_jvmObject.Invoke("getThresholds"));
+
+        /// <summary>
+        /// The <see cref="Binarizer"/> will create a new column in the DataFrame, this is the
+        /// name of the new column.
+        /// </summary>
+        /// <returns>string, the output column</returns>
+        public string GetOutputCol() => (string)(_jvmObject.Invoke("getOutputCol"));
+
+        /// <summary>
+        /// The <see cref="Binarizer"/> will create a new column in the DataFrame, this is the
+        /// name of the new column.
+        /// </summary>
+        /// <param name="value">The name of the new column</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetOutputCol(string value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setOutputCol", value));
+        
+        /// <summary>
+        /// The <see cref="Binarizer"/> will create a new columns in the DataFrame, this is the
+        /// name of the new column.
+        /// </summary>
+        /// <returns>array of strings, the output column</returns>
+        public string[] GetOutputCols() => (string[])(_jvmObject.Invoke("getOutputCols"));
+
+        /// <summary>
+        /// The <see cref="Binarizer"/> will create a new columns in the DataFrame, this is the
+        /// name of the new column.
+        /// </summary>
+        /// <param name="value">The name of the new columns</param>
+        /// <returns>New <see cref="Binarizer"/> object</returns>
+        public Binarizer SetOutputCols(string[] value) => 
+            WrapAsBinarizer(_jvmObject.Invoke("setOutputCols", value));
+
+        /// <summary>
+        /// Executes the <see cref="Binarizer"/> and transforms the DataFrame to include the new
+        /// column
+        /// </summary>
+        /// <param name="source">The DataFrame to transform</param>
+        /// <returns>
+        /// New <see cref="DataFrame"/> object with the source <see cref="DataFrame"/> transformed
+        /// </returns>
+        public DataFrame Transform(DataFrame source) => 
+            new DataFrame((JvmObjectReference)_jvmObject.Invoke("transform", source));
+        
+        /// <summary>
+        /// Executes the <see cref="Binarizer"/> and transforms the schema.
+        /// </summary>
+        /// <param name="value">The Schema to be transformed</param>
+        /// <returns>
+        /// New <see cref="StructType"/> object with the schema <see cref="StructType"/> transformed.
+        /// </returns>
+        public StructType TransformSchema(StructType value) =>
+            new StructType(
+                (JvmObjectReference)_jvmObject.Invoke(
+                    "transformSchema",
+                    DataType.FromJson(_jvmObject.Jvm, value.Json)));
+
+        /// <summary>
+        /// Loads the <see cref="Binarizer"/> that was previously saved using Save
+        /// </summary>
+        /// <param name="path">The path the previous <see cref="Binarizer"/> was saved to</param>
+        /// <returns>New <see cref="Binarizer"/> object, loaded from path</returns>
+        public static Binarizer Load(string path)
+        {
+            return WrapAsBinarizer(
+                SparkEnvironment.JvmBridge.CallStaticJavaMethod(
+                    s_binarizerClassName, "load", path));
+        }
+        
+        private static Binarizer WrapAsBinarizer(object obj) => 
+            new Binarizer((JvmObjectReference)obj);
+    }
+}