如何在sparkml分类中指定“正类”?

如何在sparkml(二进制)分类中指定“正类”?(或者可能是:一个 MulticlassClassificationEvaluator

假设我们正在训练一个模型,以达到二元分类问题中的目标精度,如。。。

label_idxer = StringIndexer(inputCol="response",
                            outputCol="label").fit(df_spark)
# we fit so we can get the "labels" attribute to inform reconversion stage

feature_idxer = StringIndexer(inputCols=cat_features,
                              outputCols=[f"{f}_IDX" for f in cat_features],
                              handleInvalid="keep")

onehotencoder = OneHotEncoder(inputCols=feature_idxer.getOutputCols(),
                              outputCols=[f"{f}_OHE" for f in feature_idxer.getOutputCols()])

assembler = VectorAssembler(inputCols=(num_features + onehotencoder.getOutputCols()),
                            outputCol="features")

rf = RandomForestClassifier(labelCol=label_idxer.getOutputCol(),
                            featuresCol=assembler.getOutputCol(),
                            seed=123456789)

label_converter = IndexToString(inputCol=rf.getPredictionCol(),
                                outputCol="prediction_label",
                                labels=label_idxer.labels)

pipeline = Pipeline(stages=[label_idxer, feature_idxer, onehotencoder,
                            assembler,
                            rf,
                            label_converter])  # type: pyspark.ml.pipeline.PipelineModel

crossval = CrossValidator(estimator=pipeline,
                          evaluator=MulticlassClassificationEvaluator(
                              labelCol=rf.getLabelCol(),
                              predictionCol=rf.getPredictionCol(),
                              metricName="weightedPrecision"),  
                          numFolds=3)

(train_u, test_u) = dff.randomSplit([0.8, 0.2])
model = crossval.fit(train_u)

我知道。。。

Precision = TP / (TP + FP) 

…但是如何将特定的类标签指定为精度目标的“正类”?(目前,IDK在培训中实际使用了哪个响应值,以及如何判断)。