MulticlassClassificationEvaluator

Companion object MulticlassClassificationEvaluator

class MulticlassClassificationEvaluator extends Evaluator with HasPredictionCol with HasLabelCol with HasWeightCol with HasProbabilityCol with DefaultParamsWritable

Evaluator for multiclass classification, which expects input columns: prediction, label, weight (optional) and probability (only for logLoss).

Annotations: @Since( "1.5.0" )
Source: MulticlassClassificationEvaluator.scala

Linear Supertypes

DefaultParamsWritable, MLWritable, HasProbabilityCol, HasWeightCol, HasLabelCol, HasPredictionCol, Evaluator, Params, Serializable, Serializable, Identifiable, AnyRef, Any

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MulticlassClassificationEvaluator
DefaultParamsWritable
MLWritable
HasProbabilityCol
HasWeightCol
HasLabelCol
HasPredictionCol
Evaluator
Params
Serializable
Serializable
Identifiable
AnyRef
Any

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Public
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Instance Constructors

new MulticlassClassificationEvaluator()

Annotations
@Since( "1.5.0" )
new MulticlassClassificationEvaluator(uid: String)

Annotations
@Since( "1.5.0" )

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final val beta: DoubleParam
The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel".
The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel". Must be greater than 0. The default value is 1.

Annotations
@Since( "3.0.0" )
final def clear(param: Param[_]): MulticlassClassificationEvaluator.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): MulticlassClassificationEvaluator
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
MulticlassClassificationEvaluator → Evaluator → Params
Annotations
@Since( "1.5.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final val eps: DoubleParam
param for eps.
param for eps. log-loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). Must be in range (0, 0.5). The default value is 1e-15.

Annotations
@Since( "3.0.0" )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
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
MulticlassClassificationEvaluator → Evaluator
Annotations
@Since( "2.0.0" )
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" )
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
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
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
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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
def getBeta: Double

Annotations
@Since( "3.0.0" )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
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
def getEps: Double

Annotations
@Since( "3.0.0" )
final def getLabelCol: String

Definition Classes
HasLabelCol
def getMetricLabel: Double

Annotations
@Since( "3.0.0" )
def getMetricName: String

Annotations
@Since( "1.5.0" )
def getMetrics(dataset: Dataset[_]): MulticlassMetrics
Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.
Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.
dataset
a dataset that contains labels/observations and predictions.
returns
MulticlassMetrics

Annotations
@Since( "3.1.0" )
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
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getWeightCol: String

Definition Classes
HasWeightCol
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
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
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
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
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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
MulticlassClassificationEvaluator → Evaluator
Annotations
@Since( "1.5.0" )
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol
final val metricLabel: DoubleParam
The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel".
The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel". Must be greater than or equal to 0. The default value is 0.

Annotations
@Since( "3.0.0" )
val metricName: Param[String]
param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")
param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")

Annotations
@Since( "1.5.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
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.
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol
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( ... )
final def set(paramPair: ParamPair[_]): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): MulticlassClassificationEvaluator.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
def setBeta(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
final def setDefault(paramPairs: ParamPair[_]*): MulticlassClassificationEvaluator.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
def setEps(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setLabelCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setMetricLabel(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setMetricName(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setPredictionCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setProbabilityCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setWeightCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
MulticlassClassificationEvaluator → Identifiable → AnyRef → Any
Annotations
@Since( "3.0.0" )
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
MulticlassClassificationEvaluator → Identifiable
Annotations
@Since( "1.5.0" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
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
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Value Members

def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from MLWritable

Value Members

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( ... )

Inherited from HasProbabilityCol

Value Members

final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol

Inherited from HasWeightCol

Value Members

final def getWeightCol: String

Definition Classes
HasWeightCol
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

Inherited from HasLabelCol

Value Members

final def getLabelCol: String

Definition Classes
HasLabelCol
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol

Inherited from HasPredictionCol

Value Members

final def getPredictionCol: String

Definition Classes
HasPredictionCol
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol

Inherited from Evaluator

Value Members

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" )

Inherited from Params

Value Members

final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def clear(param: Param[_]): MulticlassClassificationEvaluator.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
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
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
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
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
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
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
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
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
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
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
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
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.
final def set(paramPair: ParamPair[_]): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): MulticlassClassificationEvaluator.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): MulticlassClassificationEvaluator.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params

Inherited from AnyRef

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Inherited from Any

Value Members

final def asInstanceOf[T0]: T0

Definition Classes
Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any

Parameters

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

final val beta: DoubleParam
The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel".
The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel". Must be greater than 0. The default value is 1.

Annotations
@Since( "3.0.0" )
final val eps: DoubleParam
param for eps.
param for eps. log-loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). Must be in range (0, 0.5). The default value is 1e-15.

Annotations
@Since( "3.0.0" )
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol
final val metricLabel: DoubleParam
The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel".
The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel". Must be greater than or equal to 0. The default value is 0.

Annotations
@Since( "3.0.0" )
val metricName: Param[String]
param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")
param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")

Annotations
@Since( "1.5.0" )
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol
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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): MulticlassClassificationEvaluator.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): MulticlassClassificationEvaluator
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
MulticlassClassificationEvaluator → Evaluator → Params
Annotations
@Since( "1.5.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
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
MulticlassClassificationEvaluator → Evaluator
Annotations
@Since( "2.0.0" )
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" )
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
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
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
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
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
def getMetrics(dataset: Dataset[_]): MulticlassMetrics
Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.
Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.
dataset
a dataset that contains labels/observations and predictions.
returns
MulticlassMetrics

Annotations
@Since( "3.1.0" )
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
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
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
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
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
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
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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
MulticlassClassificationEvaluator → Evaluator
Annotations
@Since( "1.5.0" )
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
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.
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( ... )
final def set(paramPair: ParamPair[_]): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): MulticlassClassificationEvaluator.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): MulticlassClassificationEvaluator.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
MulticlassClassificationEvaluator → Identifiable → AnyRef → Any
Annotations
@Since( "3.0.0" )
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
MulticlassClassificationEvaluator → Identifiable
Annotations
@Since( "1.5.0" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Parameter setters

def setBeta(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setEps(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setLabelCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setMetricLabel(value: Double): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setMetricName(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setPredictionCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "1.5.0" )
def setProbabilityCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )
def setWeightCol(value: String): MulticlassClassificationEvaluator.this.type

Annotations
@Since( "3.0.0" )

Parameter getters

def getBeta: Double

Annotations
@Since( "3.0.0" )
def getEps: Double

Annotations
@Since( "3.0.0" )
final def getLabelCol: String

Definition Classes
HasLabelCol
def getMetricLabel: Double

Annotations
@Since( "3.0.0" )
def getMetricName: String

Annotations
@Since( "1.5.0" )
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getWeightCol: String

Definition Classes
HasWeightCol

Packages

MulticlassClassificationEvaluator 

Companion object MulticlassClassificationEvaluator

class MulticlassClassificationEvaluator extends Evaluator with HasPredictionCol with HasLabelCol with HasWeightCol with HasProbabilityCol with DefaultParamsWritable

Instance Constructors

Value Members

Inherited from DefaultParamsWritable

Value Members

Inherited from MLWritable

Value Members

Inherited from HasProbabilityCol

Value Members

Inherited from HasWeightCol

Value Members

Inherited from HasLabelCol

Value Members

Inherited from HasPredictionCol

Value Members

Inherited from Evaluator

Value Members

Inherited from Params

Value Members

Inherited from AnyRef

Value Members

Inherited from Any

Value Members

Parameters

Members

Parameter setters

Parameter getters

MulticlassClassificationEvaluator