updated based on comments

mllib-local/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala

@@ -31,7 +31,7 @@ object TestingUtils {
    * Note that if x or y is extremely close to zero, i.e., smaller than Double.MinPositiveValue,
    * the relative tolerance is meaningless, so the exception will be raised to warn users.
    */
-  private[ml] def RelativeErrorComparison(x: Double, y: Double, eps: Double): Boolean = {
+  private def RelativeErrorComparison(x: Double, y: Double, eps: Double): Boolean = {
     // Special case for NaNs
     if (x.isNaN && y.isNaN) {
       return true

mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala

@@ -103,7 +103,7 @@ abstract class Classifier[
    *                  [0, numClasses).
    */
   protected def validateLabel(label: Double, numClasses: Int): Unit = {
-    require(label % 1 == 0 && label >= 0 && label < numClasses, s"Classifier was given" +
+    require(label.toLong == label && label >= 0 && label < numClasses, s"Classifier was given" +
       s" dataset with invalid label $label.  Labels must be integers in range" +
       s" [0, $numClasses).")
   }

mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala

@@ -26,7 +26,7 @@ import org.apache.spark.ml.linalg.Vector
  * @param weight The weight of this instance.
  * @param features The vector of features for this data point.
  */
-private[spark] case class Instance(label: Double, weight: Double, features: Vector)
+private[ml] case class Instance(label: Double, weight: Double, features: Vector)
 
 /**
  * Case class that represents an instance of data point with

mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala

@@ -23,7 +23,7 @@ import scala.util.Try
 import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.internal.Logging
-import org.apache.spark.ml.feature.{Instance => NewInstance}
+import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.tree.{DecisionTreeModel => NewDTModel, TreeEnsembleParams => NewRFParams}
 import org.apache.spark.ml.tree.impl.{RandomForest => NewRandomForest}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -93,7 +93,7 @@ private class RandomForest (
    */
   def run(input: RDD[LabeledPoint]): RandomForestModel = {
     val instances = input.map { case LabeledPoint(label, features) =>
-      NewInstance(label, 1.0, features.asML)
+      Instance(label, 1.0, features.asML)
     }
     val trees: Array[NewDTModel] =
       NewRandomForest.run(instances, strategy, numTrees, featureSubsetStrategy, seed.toLong, None)

mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala

@@ -180,14 +180,14 @@ class DecisionTreeRegressorSuite extends MLTest with DefaultReadWriteTest {
         .setSeed(123)
       MLTestingUtils.testArbitrarilyScaledWeights[DecisionTreeRegressionModel,
         DecisionTreeRegressor](df.as[LabeledPoint], estimator,
-        MLTestingUtils.modelPredictionEquals(df, RelativeErrorComparison(_, _, 0.05), 0.9))
+        MLTestingUtils.modelPredictionEquals(df, _ ~== _ relTol 0.05, 0.9))
       MLTestingUtils.testOutliersWithSmallWeights[DecisionTreeRegressionModel,
         DecisionTreeRegressor](df.as[LabeledPoint], estimator, numClasses,
-        MLTestingUtils.modelPredictionEquals(df, RelativeErrorComparison(_, _, 0.1), 0.8),
+        MLTestingUtils.modelPredictionEquals(df, _ ~== _ relTol 0.1, 0.8),
         outlierRatio = 2)
       MLTestingUtils.testOversamplingVsWeighting[DecisionTreeRegressionModel,
         DecisionTreeRegressor](df.as[LabeledPoint], estimator,
-        MLTestingUtils.modelPredictionEquals(df, RelativeErrorComparison(_, _, 0.01), 1.0), seed)
+        MLTestingUtils.modelPredictionEquals(df, _ ~== _ relTol 0.01, 1.0), seed)
     }
   }