diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index cd0b7d3ff8aa..09f3f94d346b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -109,12 +109,13 @@ private[regression] trait LinearRegressionParams extends PredictorParams
       schema: StructType,
       fitting: Boolean,
       featuresDataType: DataType): StructType = {
-    if ($(loss) == Huber) {
-      require($(solver)!= Normal, "LinearRegression with huber loss doesn't support " +
-        "normal solver, please change solver to auto or l-bfgs.")
-      require($(elasticNetParam) == 0.0, "LinearRegression with huber loss only supports " +
-        s"L2 regularization, but got elasticNetParam = $getElasticNetParam.")
-
+    if (fitting) {
+      if ($(loss) == Huber) {
+        require($(solver)!= Normal, "LinearRegression with huber loss doesn't support " +
+          "normal solver, please change solver to auto or l-bfgs.")
+        require($(elasticNetParam) == 0.0, "LinearRegression with huber loss only supports " +
+          s"L2 regularization, but got elasticNetParam = $getElasticNetParam.")
+      }
     }
     super.validateAndTransformSchema(schema, fitting, featuresDataType)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index d3df0e5b4448..82d984933d81 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -187,6 +187,18 @@ class LinearRegressionSuite extends MLTest with DefaultReadWriteTest with PMMLRe
     assert(model.numFeatures === numFeatures)
   }
 
+  test("linear regression: can transform data with LinearRegressionModel") {
+    withClue("training related params like loss are only validated during fitting phase") {
+      val original = new LinearRegression().fit(datasetWithDenseFeature)
+
+      val deserialized = new LinearRegressionModel(uid = original.uid,
+        coefficients = original.coefficients,
+        intercept = original.intercept)
+      val output = deserialized.transform(datasetWithDenseFeature)
+      assert(output.collect().size > 0) // simple assertion to ensure no exception thrown
+    }
+  }
+
   test("linear regression: illegal params") {
     withClue("LinearRegression with huber loss only supports L2 regularization") {
       intercept[IllegalArgumentException] {