问题描述
我已经训练了一个模型，并希望计算几个重要指标，例如accuracy，precision，recall和f1 score。
我遵循的过程是：
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression(featuresCol='features',labelCol='label')
lrModel = lr.fit(train)
lrPredictions = lrModel.transform(test)
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
eval_accuracy = MulticlassClassificationEvaluator(labelCol="label",predictionCol="prediction",metricName="accuracy")
eval_precision = MulticlassClassificationEvaluator(labelCol="label",metricName="precision")
eval_recall = MulticlassClassificationEvaluator(labelCol="label",metricName="recall")
eval_f1 = MulticlassClassificationEvaluator(labelCol="label",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)
但是，它只能计算accuracy和auc，而不能计算其他三个。我应该在这里修改什么？
解决方法
根据docs，对于F1度量，精度和查全率，MulticlassClassificationEvaluator的相关参数应分别为
metricName="f1"
metricName="precisionByLabel"
metricName="recallByLabel"