diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs
index 2552c5be98..594b95bd03 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs
@@ -54,8 +54,6 @@ public void MatrixFactorization_Estimator()
         }
 
         [MatrixFactorizationFact]
-        //Skipping test temporarily. This test will be re-enabled once the cause of failures has been determined
-        [Trait("Category", "SkipInCI")]
         public void MatrixFactorizationSimpleTrainAndPredict()
         {
             var mlContext = new MLContext(seed: 1);
@@ -94,7 +92,7 @@ public void MatrixFactorizationSimpleTrainAndPredict()
             var rightMatrix = model.Model.RightFactorMatrix;
             Assert.Equal(leftMatrix.Count, model.Model.NumberOfRows * model.Model.ApproximationRank);
             Assert.Equal(rightMatrix.Count, model.Model.NumberOfColumns * model.Model.ApproximationRank);
-            // MF produce different matrixes on different platforms, so at least test thier content on windows.
+            // MF produce different matrixes on different platforms, so at least test their content on windows.
             if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
             {
                 Assert.Equal(0.33491, leftMatrix[0], 5);
@@ -124,12 +122,14 @@ public void MatrixFactorizationSimpleTrainAndPredict()
             var metrices = mlContext.Recommendation().Evaluate(prediction, labelColumnName: labelColumnName, scoreColumnName: scoreColumnName);
 
             // Determine if the selected metric is reasonable for different platforms
-            double tolerance = Math.Pow(10, -7);
+            double windowsTolerance = Math.Pow(10, -7);
+            // 1e-7 is too small for Linux, so we try 1e-4
+            double linuxTolerance = Math.Pow(10, -4);
             if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
             {
                 // Linux case
                 var expectedUnixL2Error = 0.614457914950479; // Linux baseline
-                Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - tolerance, expectedUnixL2Error + tolerance);
+                Assert.InRange(metrices.MeanSquaredError, expectedUnixL2Error - linuxTolerance, expectedUnixL2Error + linuxTolerance);
             }
             else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
             {
@@ -142,7 +142,7 @@ public void MatrixFactorizationSimpleTrainAndPredict()
             {
                 // Windows case
                 var expectedWindowsL2Error = 0.6098110249191965; // Windows baseline
-                Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - tolerance, expectedWindowsL2Error + tolerance);
+                Assert.InRange(metrices.MeanSquaredError, expectedWindowsL2Error - windowsTolerance, expectedWindowsL2Error + windowsTolerance);
             }
 
             var modelWithValidation = pipeline.Fit(data, testData);