diff --git a/src/main/scala/com/databricks/spark/sql/perf/mllib/regression/LinearRegression.scala b/src/main/scala/com/databricks/spark/sql/perf/mllib/regression/LinearRegression.scala
new file mode 100644
index 0000000..8acbb51
--- /dev/null
+++ b/src/main/scala/com/databricks/spark/sql/perf/mllib/regression/LinearRegression.scala
@@ -0,0 +1,46 @@
+package com.databricks.spark.sql.perf.mllib.regression
+
+import org.apache.spark.ml
+import org.apache.spark.ml.evaluation.{Evaluator, RegressionEvaluator}
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.{Estimator, ModelBuilder, Transformer}
+
+import com.databricks.spark.sql.perf.mllib.OptionImplicits._
+import com.databricks.spark.sql.perf.mllib._
+import com.databricks.spark.sql.perf.mllib.data.DataGenerator
+
+
+object LinearRegression extends BenchmarkAlgorithm with TestFromTraining with
+  TrainingSetFromTransformer with ScoringWithEvaluator {
+
+  override protected def initialData(ctx: MLBenchContext) = {
+    import ctx.params._
+    DataGenerator.generateContinuousFeatures(
+      ctx.sqlContext,
+      numExamples,
+      ctx.seed(),
+      numPartitions,
+      numFeatures)
+  }
+
+  override protected def trueModel(ctx: MLBenchContext): Transformer = {
+    val rng = ctx.newGenerator()
+    val coefficients =
+      Vectors.dense(Array.fill[Double](ctx.params.numFeatures)(2 * rng.nextDouble() - 1))
+    // Small intercept to prevent some skew in the data.
+    val intercept = 0.01 * (2 * rng.nextDouble - 1)
+    ModelBuilder.newLinearRegressionModel(coefficients, intercept)
+  }
+
+  override def getEstimator(ctx: MLBenchContext): Estimator[_] = {
+    import ctx.params._
+    new ml.regression.LinearRegression()
+      .setSolver("l-bfgs")
+      .setRegParam(regParam)
+      .setMaxIter(maxIter)
+      .setTol(tol)
+  }
+
+  override protected def evaluator(ctx: MLBenchContext): Evaluator =
+    new RegressionEvaluator()
+}
diff --git a/src/main/scala/configs/mllib-small.yaml b/src/main/scala/configs/mllib-small.yaml
index 6f1b023..24408f8 100644
--- a/src/main/scala/configs/mllib-small.yaml
+++ b/src/main/scala/configs/mllib-small.yaml
@@ -63,3 +63,11 @@ benchmarks:
       numClasses: 4
       numFeatures: 5
       maxIter: 3
+  - name: regression.LinearRegression
+    params:
+      numExamples: 100
+      numTestExamples: 100
+      numFeatures: 100
+      regParam: 0.1
+      tol: [0.0]
+      maxIter: 10
diff --git a/src/main/scala/org/apache/spark/ml/ModelBuilder.scala b/src/main/scala/org/apache/spark/ml/ModelBuilder.scala
index 7d0143c..ccddb57 100644
--- a/src/main/scala/org/apache/spark/ml/ModelBuilder.scala
+++ b/src/main/scala/org/apache/spark/ml/ModelBuilder.scala
@@ -2,8 +2,7 @@ package org.apache.spark.ml
 
 import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, LogisticRegressionModel}
 import org.apache.spark.ml.linalg.Vector
-import org.apache.spark.ml.regression.GeneralizedLinearRegressionModel
-import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.regression.{LinearRegressionModel, GeneralizedLinearRegressionModel, DecisionTreeRegressionModel}
 import org.apache.spark.ml.tree._
 import org.apache.spark.mllib.random.RandomDataGenerator
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
@@ -20,6 +19,12 @@ object ModelBuilder {
     new LogisticRegressionModel("lr", coefficients, intercept)
   }
 
+  def newLinearRegressionModel(
+      coefficients: Vector,
+      intercept: Double): LinearRegressionModel = {
+    new LinearRegressionModel("linr", coefficients, intercept)
+  }
+
   def newGLR(
       coefficients: Vector,
       intercept: Double): GeneralizedLinearRegressionModel =