[SPARK-34591][ML] Add decision tree pruning as a parameter

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

@@ -74,6 +74,10 @@ class DecisionTreeClassifier @Since("1.4.0") (
   @Since("1.4.0")
   def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
 
+  /** @group setParam */
+  @Since("4.3.0")
+  def setPruneTree(value: Boolean): this.type = set(pruneTree, value)
+
   /** @group expertSetParam */
   @Since("1.4.0")
   def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
@@ -134,9 +138,11 @@ class DecisionTreeClassifier @Since("1.4.0") (
 
     val strategy = getOldStrategy(categoricalFeatures, numClasses)
     require(!strategy.bootstrap, "DecisionTreeClassifier does not need bootstrap sampling")
+    strategy.pruneTree = $(pruneTree)
+
     instr.logNumClasses(numClasses)
     instr.logParams(this, labelCol, featuresCol, predictionCol, rawPredictionCol,
-      probabilityCol, leafCol, maxDepth, maxBins, minInstancesPerNode, minInfoGain,
+      probabilityCol, leafCol, maxDepth, maxBins, minInstancesPerNode, minInfoGain, pruneTree,
       maxMemoryInMB, cacheNodeIds, checkpointInterval, impurity, seed, thresholds)
 
     val trees = RandomForest.run(instances, strategy, numTrees = 1, featureSubsetStrategy = "all",

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

@@ -76,6 +76,10 @@ class RandomForestClassifier @Since("1.4.0") (
   @Since("1.4.0")
   def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
 
+  /** @group setParam */
+  @Since("4.3.0")
+  def setPruneTree(value: Boolean): this.type = set(pruneTree, value)
+
   /** @group expertSetParam */
   @Since("1.4.0")
   def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
@@ -159,10 +163,11 @@ class RandomForestClassifier @Since("1.4.0") (
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
     strategy.bootstrap = $(bootstrap)
+    strategy.pruneTree = $(pruneTree)
 
     instr.logParams(this, labelCol, featuresCol, weightCol, predictionCol, probabilityCol,
       rawPredictionCol, leafCol, impurity, numTrees, featureSubsetStrategy, maxDepth, maxBins,
-      maxMemoryInMB, minInfoGain, minInstancesPerNode, minWeightFractionPerNode, seed,
+      maxMemoryInMB, minInfoGain, pruneTree, minInstancesPerNode, minWeightFractionPerNode, seed,
       subsamplingRate, thresholds, cacheNodeIds, checkpointInterval, bootstrap)
 
     val trees = RandomForest