我正在尝试提取使用Pyspark训练的随机森林分类器模型的特征重要性。我参考了以下文章来获取我训练的随机森林模型的特征重要性得分。
然而，当我使用该文章中描述的方法时，我遇到了以下错误
'CrossValidatorModel' object has no attribute 'featureImportances'这是我用来训练模型的代码
cols = new_data.columnsstages = []label_stringIdx = StringIndexer(inputCol = 'Bought_Fibre', outputCol = 'label')stages += [label_stringIdx]numericCols = new_data.schema.names[1:-1]assembler = VectorAssembler(inputCols=numericCols, outputCol="features")stages += [assembler]pipeline = Pipeline(stages = stages)pipelineModel = pipeline.fit(new_data)new_data.fillna(0, subset=cols)new_data = pipelineModel.transform(new_data)new_data.fillna(0, subset=cols)new_data.printSchema()train_initial, test = new_data.randomSplit([0.7, 0.3], seed = 1045)train_initial.groupby('label').count().toPandas()test.groupby('label').count().toPandas()train_sampled = train_initial.sampleBy("label", fractions={0: 0.1, 1: 1.0}, seed=0)train_sampled.groupBy("label").count().orderBy("label").show()labelIndexer = StringIndexer(inputCol='label', outputCol='indexedLabel').fit(train_sampled)featureIndexer = VectorIndexer(inputCol='features', outputCol='indexedFeatures', maxCategories=2).fit(train_sampled)from pyspark.ml.classification import RandomForestClassifierrf_model = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel", labels=labelIndexer.labels)pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf_model, labelConverter])paramGrid = ParamGridBuilder() \ .addGrid(rf_model.numTrees, [ 200, 400,600,800,1000]) \ .addGrid(rf_model.impurity,['entropy','gini']) \ .addGrid(rf_model.maxDepth,[2,3,4,5]) \ .build()crossval = CrossValidator(estimator=pipeline, estimatorParamMaps=paramGrid, evaluator=BinaryClassificationEvaluator(), numFolds=5) train_model = crossval.fit(train_sampled)请帮助解决上述错误，并帮助提取特征
回答：
这是因为CrossValidatorModel没有特征重要性的属性，但RandomForestModel模型有这个属性。
由于您使用Pipeline和CrossValidator来拟合数据，您需要获取最佳拟合模型中的底层阶段：
# '2' 是 Pipeline 中您的 RandomForestModel 的索引your_model = cvModel.bestModel.stages[2] var_imp = your_model.featureImportances