f1score的Spark mllib阈值

import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils

// 加载数据集
val data = MLUtils.loadLibSVMFile(sc, "data.txt")
val parsedData = data.map { line =>
  val parts = line.split(' ')
  LabeledPoint(parts(0).toDouble, parts(1).split(' ').map(_.toDouble))
}

// 训练模型
val model = ??? // 使用Spark MLlib训练模型的代码

// 获取模型预测结果的概率值
val predictionAndLabels = parsedData.map { point =>
  val prediction = model.predict(point.features)
  (prediction, point.label)
}

// 计算F1 Score
val metrics = new BinaryClassificationMetrics(predictionAndLabels)
val f1Score = metrics.fMeasureByThreshold().maxBy(_._2)._2

println("F1 Score: " + f1Score)