class pyspark.ml.evaluation.BinaryClassificationEvaluator(*, rawPredictionCol='rawPrediction', labelCol='label', metricName='areaUnderROC', weightCol=None, numBins=1000) 二进制分类的评估器,它需要输入列 rawPrediction、标签和可选的权重列。 rawPrediction 列可以是 double 类型(二进制 0/1 预测,或标签 1...
Evaluate the Random Forest model with BinaryClassificationEvaluator. from pyspark.ml.evaluation import BinaryClassificationEvaluator # Evaluate model evaluator = BinaryClassificationEvaluator() evaluator.evaluate(predictions) Now tune the model with ParamGridBuilder and CrossValidator. With three values for max...
classificationlogistic-regressionfactorization-machinesdecision-tree-classifiermultilayer-perceptronone-vs-restpyspark-mlliblinear-support-vector-machinedecision-tree-classificationrandom-forest-classificationclassification-algorithmsregression-methodsgbt-classificationbinaryclassificationevaluatormulticlassclassificationevaluator...
因此我们决定通过交叉验证将f1-得分用于超参数选择，我们使用pyscem2.3和pyspark.ml，我们创建了一个Cros...