Packages

class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol with HasWeightCol with DefaultParamsWritable

Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).

Annotations
@Since( "1.2.0" )
Source
BinaryClassificationEvaluator.scala
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Inherited
  1. BinaryClassificationEvaluator
  2. DefaultParamsWritable
  3. MLWritable
  4. HasWeightCol
  5. HasLabelCol
  6. HasRawPredictionCol
  7. Evaluator
  8. Params
  9. Serializable
  10. Serializable
  11. Identifiable
  12. AnyRef
  13. Any
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Visibility
  1. Public
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  2. val metricName: Param[String]

    param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

    param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

    Annotations
    @Since( "1.2.0" )
  3. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  4. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol

Members

  1. final def clear(param: Param[_]): BinaryClassificationEvaluator.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def copy(extra: ParamMap): BinaryClassificationEvaluator

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    BinaryClassificationEvaluatorEvaluatorParams
    Annotations
    @Since( "1.4.1" )
  3. def evaluate(dataset: Dataset[_]): Double

    Evaluates model output and returns a scalar metric.

    Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.

    dataset

    a dataset that contains labels/observations and predictions.

    returns

    metric

    Definition Classes
    BinaryClassificationEvaluatorEvaluator
    Annotations
    @Since( "2.0.0" )
  4. def evaluate(dataset: Dataset[_], paramMap: ParamMap): Double

    Evaluates model output and returns a scalar metric.

    Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.

    dataset

    a dataset that contains labels/observations and predictions.

    paramMap

    parameter map that specifies the input columns and output metrics

    returns

    metric

    Definition Classes
    Evaluator
    Annotations
    @Since( "2.0.0" )
  5. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  6. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  9. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  10. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  11. def getMetrics(dataset: Dataset[_]): BinaryClassificationMetrics

    Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.

    Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.

    dataset

    a dataset that contains labels/observations and predictions.

    returns

    BinaryClassificationMetrics

    Annotations
    @Since( "3.1.0" )
  12. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  13. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  14. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  15. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  16. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  17. def isLargerBetter: Boolean

    Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false).

    Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false). A given evaluator may support multiple metrics which may be maximized or minimized.

    Definition Classes
    BinaryClassificationEvaluatorEvaluator
    Annotations
    @Since( "1.5.0" )
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  20. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  21. final def set[T](param: Param[T], value: T): BinaryClassificationEvaluator.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def toString(): String
    Definition Classes
    BinaryClassificationEvaluatorIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  23. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    BinaryClassificationEvaluatorIdentifiable
    Annotations
    @Since( "1.4.0" )
  24. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setLabelCol(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "1.2.0" )
  2. def setMetricName(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "1.2.0" )
  3. def setRawPredictionCol(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "1.5.0" )
  4. def setWeightCol(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "3.0.0" )

Parameter getters

  1. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  2. def getMetricName: String

    Annotations
    @Since( "1.2.0" )
  3. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  4. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. val numBins: IntParam

    param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation.

    param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation. If 0, no down-sampling will occur. Default: 1000.

    Annotations
    @Since( "3.0.0" )

(expert-only) Parameter setters

  1. def setNumBins(value: Int): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "3.0.0" )

(expert-only) Parameter getters

  1. def getNumBins: Int

    Annotations
    @Since( "3.0.0" )