本文整理汇总了Scala中org.apache.spark.ml.evaluation.BinaryClassificationEvaluator类的典型用法代码示例。如果您正苦于以下问题：Scala BinaryClassificationEvaluator类的具体用法？Scala BinaryClassificationEvaluator怎么用？Scala BinaryClassificationEvaluator使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了BinaryClassificationEvaluator类的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Scala代码示例。
示例1: LRCV
//设置package包名称以及导入依赖的类
package com.ferhtaydn.rater
import org.apache.spark.SparkContext
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{ StringIndexerModel, VectorAssembler }
import org.apache.spark.ml.tuning.{ CrossValidator, CrossValidatorModel, ParamGridBuilder }
import org.apache.spark.mllib.linalg.Matrix
import org.apache.spark.sql.{ DataFrame, Row, SQLContext }
class LRCV(sc: SparkContext) {
implicit val sqlContext = new SQLContext(sc)
val lr = new LogisticRegression().setMaxIter(10).setFeaturesCol("scaledFeatures")
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, Array(0.1, 0.01))
.build()
val assembler = new VectorAssembler()
.setInputCols(Array("gender", "age", "weight", "height", "indexedJob"))
.setOutputCol("features")
val pipeline = new Pipeline()
.setStages(Array(assembler, standardScaler("features"), lr))
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(10)
def train(df: DataFrame): (StringIndexerModel, CrossValidatorModel, Matrix) = {
// need to index strings on all data to not missing the job fields.
// other alternative can be manually assign values for each job like gender.
val indexerModel = stringIndexer("job").fit(df)
val indexed = indexerModel.transform(df)
val splits = indexed.randomSplit(Array(0.8, 0.2))
val training = splits(0).cache()
val test = splits(1)
val cvModel = cv.fit(training)
val predictionAndLabels = cvModel
.transform(test)
.select("label", "prediction").map {
case Row(label: Double, prediction: Double) ?
(prediction, label)
}
printBinaryMetrics(predictionAndLabels)
(indexerModel, cvModel, confusionMatrix(predictionAndLabels))
}
}