Using getOldLossType as per comments

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

@@ -279,7 +279,7 @@ class GBTClassificationModel private[ml](
       // and negative result:
       // p-(x) = 1 / (1 + e^(2 * F(x)))
       case dv: DenseVector =>
-        dv.values(0) = classProbability(getLossType, dv.values(0))
+        dv.values(0) = getOldLossType.computeProbability(dv.values(0))
         dv.values(1) = 1.0 - dv.values(0)
         dv
       case sv: SparseVector =>
@@ -315,13 +315,6 @@ class GBTClassificationModel private[ml](
   @Since("2.0.0")
   lazy val featureImportances: Vector = TreeEnsembleModel.featureImportances(trees, numFeatures)
 
-  private def classProbability(loss: String, rawPrediction: Double): Double = {
-    loss match {
-      case "logistic" => LogLoss.computeProbability(rawPrediction)
-      case _ => throw new Exception("Only logistic loss is supported ...")
-    }
-  }
-
   private def margin(features: Vector): Double = {
     val treePredictions = _trees.map(_.rootNode.predictImpl(features).prediction)
     blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1)

mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala

@@ -25,7 +25,7 @@ import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
-import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, LogLoss => OldLogLoss, Loss => OldLoss, SquaredError => OldSquaredError}
+import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, ClassificationLoss => OldClassificationLoss, LogLoss => OldLogLoss, Loss => OldLoss, SquaredError => OldSquaredError}
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 
 /**
@@ -531,7 +531,7 @@ private[ml] trait GBTClassifierParams extends GBTParams with TreeClassifierParam
   def getLossType: String = $(lossType).toLowerCase
 
   /** (private[ml]) Convert new loss to old loss. */
-  override private[ml] def getOldLossType: OldLoss = {
+  override private[ml] def getOldLossType: OldClassificationLoss = {
     getLossType match {
       case "logistic" => OldLogLoss
       case _ =>