case class LogRegRanker(train: Dataset, options: RegressionOptions) extends Ranker[LogRegModel] with Product with Serializable
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- new LogRegRanker(train: Dataset, options: RegressionOptions)
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- final def !=(arg0: Any): Boolean
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- final def eq(arg0: AnyRef): Boolean
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- def fit(): LogRegModel
- Definition Classes
- LogRegRanker → Ranker
- final def getClass(): Class[_ <: AnyRef]
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- val options: RegressionOptions
- def predict(row: RealVector, weights: RealVector, intercept: Double): Double
- def prepare(): (Array2DRowRealMatrix, ArrayRealVector)
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- def randomSample(from: Int, to: Int, count: Int): Array[Int]
- final def synchronized[T0](arg0: => T0): T0
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- val train: Dataset
- def trainBatchSGD(x: Array2DRowRealMatrix, y: RealVector, iterations: Int, batchSize: Int, lr: Double): RegWeights
- def trainSGD(x: Array2DRowRealMatrix, y: RealVector, iterations: Int, lr: Double): RegWeights
- final def wait(arg0: Long, arg1: Int): Unit
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- val x: Array2DRowRealMatrix
- val y: ArrayRealVector
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