def getBinaryClassifierModelSelectionCriteria: String = $()

in sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/LinearLearnerSageMakerEstimator.scala [58:101]

• 21 lines of code
• 1 McCabe index (conditional complexity)

  def getBinaryClassifierModelSelectionCriteria: String = $(binaryClassifierModelSelectionCriteria)

  /**
    * Applicable if binary_classifier_model_selection_criteria is precision_at_target_recall
    * Ignored otherwise. Must be in range (0, 1).
    * Default: 0.8.
    */
  val targetRecall : DoubleParam = new DoubleParam(this, "target_recall",
    "Applicable if binary_classifier_model_selection_criteria is precision_at_target_recall. " +
      "Ignored otherwise. Must be in range (0, 1).",
    ParamValidators.inRange(0.0, 1.0, false, false))
  def getTargetRecall: Double = $(targetRecall)

  /**
    * Applicable if binary_classifier_model_selection_criteria is recall_at_target_precision
    * Ignored otherwise. Must be in range (0, 1).
    * Default: 0.8.
    */
  val targetPrecision : DoubleParam = new DoubleParam(this, "target_precision",
    "Applicable if binary_classifier_model_selection_criteria is recall_at_target_precision. " +
      "Ignored otherwise. Must be in range (0, 1).",
    ParamValidators.inRange(0.0, 1.0, false, false))
  def getTargetPrecision: Double = $(targetPrecision)

  /**
    * Weight assigned to positive examples when training a binary classifier. The weight of
    * negative examples is fixed at 1. If balanced, then a weight will be selected so that errors
    * in classifying negative vs. positive examples have equal impact on the training loss.
    * If auto, the algorithm will attempt to select the weight that optimizes performance.
    * Must be string "auto", "balanced" or float > 0
    * Default: 1.0.
    */
  val positiveExampleWeightMult : Param[String] = new Param(this, "positive_example_weight_mult",
    "Weight assigned to positive examples when training a binary classifier. The weight of" +
      "negative examples is fixed at 1. If balanced, then a weight will be selected so that" +
      "errors in classifying negative vs. positive examples have equal impact on the training" +
      "loss. If auto, the algorithm will attempt to select the weight that optimizes" +
      "performance. Must be string 'auto', 'balanced' or float > 0",
    inArrayOrAboveParamValidator(Array("auto", "balanced"), 0))
  def getPositiveExampleWeightMult: String = $(positiveExampleWeightMult)
}


private[algorithms] trait MultiClassClassifierParams extends LinearLearnerParams {