我已经训练了一个模型，并希望计算几个重要的指标，如accuracy（准确率）、precision（精确率）、recall（召回率）和f1 score（F1分数）。

我遵循的过程是：

from pyspark.ml.classification import LogisticRegressionlr = LogisticRegression(featuresCol='features',labelCol='label')lrModel = lr.fit(train)lrPredictions = lrModel.transform(test)from pyspark.ml.evaluation import MulticlassClassificationEvaluatorfrom pyspark.ml.evaluation import BinaryClassificationEvaluatoreval_accuracy = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")eval_precision = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="precision")eval_recall = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="recall")eval_f1 = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="f1Measure")eval_auc = BinaryClassificationEvaluator(labelCol="label", rawPredictionCol="prediction")accuracy = eval_accuracy.evaluate(lrPredictions)precision = eval_precision.evaluate(lrPredictions)recall = eval_recall.evaluate(lrPredictions)f1score = eval_f1.evaluate(lrPredictions)auc = eval_accuracy.evaluate(lrPredictions)

然而，它只能计算accuracy（准确率）和auc（AUC），但无法计算其他三个指标。我应该如何修改代码？

回答：

根据文档，对于F1度量、精确率和召回率，MulticlassClassificationEvaluator的相关参数应分别为

metricName="f1"metricName="precisionByLabel"metricName="recallByLabel"