我有一个随机森林模型，我试图获取featureImportance向量 .
Map<Object, Object> categoricalFeaturesParam = new HashMap<>();
scala.collection.immutable.Map<Object, Object> categoricalFeatures = (scala.collection.immutable.Map<Object, Object>)
scala.collection.immutable.Map$.MODULE$.apply(JavaConversions.mapAsScalaMap(categoricalFeaturesParam).toSeq());
int numberOfClasses = 2;
RandomForestClassifier rfc = new RandomForestClassifier();
RandomForestClassificationModel rfm = RandomForestClassificationModel.fromOld(model, rfc, categoricalFeatures, numberOfClasses);
System.out.println(rfm.featureImportances());
当我运行上面的代码时，我发现featureImportance为null . 我是否需要特定设置任何内容以获取随机森林模型的特征重要性 .
尝试使用1.6版本的Spark，它在API中使用了numberOfFeatures第五个参数，但仍然将featureImportance作为null .
RandomForestClassifier rfc = getRandomForestClassifier（numTrees，maxBinSize，maxTreeDepth，seed，impurity）; RandomForestClassificationModel rfm = RandomForestClassificationModel.fromOld（model，rfc，categoricalFeatures，numberOfClasses，numberOfFeatures）;的System.out.println（rfm.featureImportances（））;
堆栈跟踪：在org.apache.spark.ml.tree.impl的org.apache.spark.ml.tree.impl.RandomForest $ .computeFeatureImportance（RandomForest.scala：1152）中的线程“main”java.lang.NullPointerException中的异常.andomForest $$ anonfun $ featureImportances $ 1.apply（RandomForest.scala：1111）org.apache.spark.ml.tree.impl.RandomForest $$ anonfun $ featureImportances $ 1.apply（RandomForest.scala：1108）at scala.collection .IndexedSeqOptimized $ class.foreach（IndexedSeqOptimized.scala：33）at scala.collection.mutable.ArrayOps $ ofRef.foreach（ArrayOps.scala：186）at org.apache.spark.ml.tree.impl.RandomForest $ .featureImportances（ RandomForest.scala：1108）org.apache.spark.ml.classification.RandomForestClassificationModel.featureImportances $ lzycompute（RandomForestClassifier.scala：237）at org.apache.spark.ml.classification.RandomForestClassificationModel.featureImportances（RandomForestClassifier.scala：237）在com.markmonitor.antifraud.ce.ml.CheckFeatureImportance.main（CheckFeatureImportance.java:49）