diff --git a/benchmarks/apache-spark/src/main/scala/org/renaissance/apache/spark/LogRegression.scala b/benchmarks/apache-spark/src/main/scala/org/renaissance/apache/spark/LogRegression.scala
index a85b3ab6..63d050f4 100644
--- a/benchmarks/apache-spark/src/main/scala/org/renaissance/apache/spark/LogRegression.scala
+++ b/benchmarks/apache-spark/src/main/scala/org/renaissance/apache/spark/LogRegression.scala
@@ -7,11 +7,10 @@ import org.renaissance.Benchmark
 import org.renaissance.Benchmark._
 import org.renaissance.BenchmarkContext
 import org.renaissance.BenchmarkResult
-import org.renaissance.BenchmarkResult.Validators
+import org.renaissance.BenchmarkResult.Assert
 import org.renaissance.License
 import org.renaissance.apache.spark.ResourceUtil.duplicateLinesFromUrl
 
-import java.nio.file.Files
 import java.nio.file.Path
 
 @Name("log-regression")
@@ -34,10 +33,35 @@ import java.nio.file.Path
   defaultValue = "20",
   summary = "Maximum number of iterations of the logistic regression algorithm."
 )
-@Configuration(name = "test", settings = Array("copy_count = 5"))
+@Parameter(name = "expected_coefficient_sum", defaultValue = "-0.0653998570980114")
+@Parameter(name = "expected_coefficient_sum_squares", defaultValue = "9.401759355004592E-5")
+@Parameter(name = "expected_intercept_value", defaultValue = "2.287050116462375")
+@Parameter(name = "expected_intercept_count", defaultValue = "1")
+@Parameter(name = "expected_class_count", defaultValue = "2")
+@Configuration(
+  name = "test",
+  settings = Array(
+    "copy_count = 5",
+    "expected_coefficient_sum = -0.06538768469885561",
+    "expected_coefficient_sum_squares = 9.395555567324299E-5",
+    "expected_intercept_value = 2.286718680950285",
+    "expected_class_count = 2"
+  )
+)
 @Configuration(name = "jmh")
 final class LogRegression extends Benchmark with SparkUtil {
 
+  // Utility class for validation.
+
+  private case class ModelSummary(
+    coefficientSum: Double,
+    coefficientSumSquares: Double,
+    coefficientCount: Int,
+    interceptValue: Double,
+    interceptCount: Int,
+    classCount: Int
+  )
+
   // TODO: Consolidate benchmark parameters across the suite.
   //  See: https://github.com/renaissance-benchmarks/renaissance/issues/27
 
@@ -45,17 +69,17 @@ final class LogRegression extends Benchmark with SparkUtil {
 
   private val inputFeatureCount = 692
 
-  private var maxIterationsParam: Int = _
-
   private val lrRegularizationParam = 0.1
 
   private val lrElasticNetMixingParam = 0.0
 
   private val lrConvergenceToleranceParam = 0.0
 
-  private var inputDataFrame: DataFrame = _
+  private var lrMaxIterationsParam: Int = _
+
+  private var expectedModelSummary: ModelSummary = _
 
-  private var outputLogisticRegression: LogisticRegressionModel = _
+  private var inputDataFrame: DataFrame = _
 
   private def loadData(inputFile: Path, featureCount: Int) = {
     sparkSession.read
@@ -67,7 +91,17 @@ final class LogRegression extends Benchmark with SparkUtil {
   override def setUpBeforeAll(bc: BenchmarkContext): Unit = {
     setUpSparkContext(bc)
 
-    maxIterationsParam = bc.parameter("max_iterations").toPositiveInteger
+    lrMaxIterationsParam = bc.parameter("max_iterations").toPositiveInteger
+
+    // Validation parameters.
+    expectedModelSummary = ModelSummary(
+      bc.parameter("expected_coefficient_sum").toDouble,
+      bc.parameter("expected_coefficient_sum_squares").toDouble,
+      inputFeatureCount,
+      bc.parameter("expected_intercept_value").toDouble,
+      bc.parameter("expected_intercept_count").toPositiveInteger,
+      bc.parameter("expected_class_count").toPositiveInteger
+    )
 
     val inputFile = duplicateLinesFromUrl(
       getClass.getResource(inputResource),
@@ -79,43 +113,81 @@ final class LogRegression extends Benchmark with SparkUtil {
   }
 
   override def run(bc: BenchmarkContext): BenchmarkResult = {
-    val lor = new LogisticRegression()
+    val logRegression = new LogisticRegression()
       .setElasticNetParam(lrElasticNetMixingParam)
       .setRegParam(lrRegularizationParam)
       .setTol(lrConvergenceToleranceParam)
-      .setMaxIter(maxIterationsParam)
-
-    outputLogisticRegression = lor.fit(inputDataFrame)
-
-    // TODO: add more in-depth validation
-    Validators.compound(
-      Validators.simple("class count", 2, outputLogisticRegression.numClasses),
-      Validators.simple(
-        "feature count",
-        inputFeatureCount,
-        outputLogisticRegression.numFeatures
-      )
+      .setMaxIter(lrMaxIterationsParam)
+
+    val logRegressionModel = logRegression.fit(inputDataFrame)
+    () => validate(logRegressionModel)
+  }
+
+  private def validate(model: LogisticRegressionModel): Unit = {
+    //
+    // Validation currently supports only binary classification which returns
+    // a single intercept value. If multinomial logistic regression is needed,
+    // the validation needs to be updated to support multiple intercept values.
+    //
+    val actualModelSummary = summarizeModel(model)
+    validateSummary(
+      expectedModelSummary,
+      actualModelSummary,
+      coefficientSumTolerance = 0.1e-14,
+      coefficientSumSquaresTolerance = 0.1e-17,
+      interceptTolerance = 0.1e-13
     )
   }
 
-  override def tearDownAfterAll(bc: BenchmarkContext): Unit = {
-    if (dumpResultsBeforeTearDown && outputLogisticRegression != null) {
-      val outputFile = bc.scratchDirectory().resolve("output.txt")
-      dumpResult(outputLogisticRegression, outputFile)
-    }
+  private def summarizeModel(model: LogisticRegressionModel): ModelSummary = {
+    val coefficients = model.coefficients.toArray
 
-    tearDownSparkContext()
+    ModelSummary(
+      coefficients.sum,
+      coefficients.map(num => num * num).sum,
+      coefficients.length,
+      model.interceptVector(0),
+      model.interceptVector.size,
+      model.numClasses
+    )
   }
 
-  private def dumpResult(lrm: LogisticRegressionModel, outputFile: Path) = {
-    val output = new StringBuilder
-    output.append(s"num features: ${lrm.numFeatures}\n")
-    output.append(s"num classes: ${lrm.numClasses}\n")
-    output.append(s"intercepts: ${lrm.interceptVector.toString}\n")
-    output.append(s"coefficients: ${lrm.coefficients.toString}\n")
+  private def validateSummary(
+    expected: ModelSummary,
+    actual: ModelSummary,
+    coefficientSumTolerance: Double,
+    coefficientSumSquaresTolerance: Double,
+    interceptTolerance: Double
+  ): Unit = {
+    Assert.assertEquals(
+      expected.coefficientSum,
+      actual.coefficientSum,
+      coefficientSumTolerance,
+      "coefficients sum"
+    )
+
+    Assert.assertEquals(
+      expected.coefficientSumSquares,
+      actual.coefficientSumSquares,
+      coefficientSumSquaresTolerance,
+      "coefficients sum of squares"
+    )
+
+    Assert.assertEquals(expected.coefficientCount, actual.coefficientCount, "coefficient count")
 
-    // Files.writeString() is only available from Java 11.
-    Files.write(outputFile, output.toString.getBytes)
+    Assert.assertEquals(
+      expected.interceptValue,
+      actual.interceptValue,
+      interceptTolerance,
+      "intercept value"
+    )
+
+    Assert.assertEquals(expected.interceptCount, actual.interceptCount, "intercept count")
+
+    Assert.assertEquals(expected.classCount, actual.classCount, "class count")
   }
 
+  override def tearDownAfterAll(bc: BenchmarkContext): Unit = {
+    tearDownSparkContext()
+  }
 }