org.apache.spark.ml.evaluation
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
- Grouped
- Alphabetic
- By Inheritance
- MulticlassClassificationEvaluator
- DefaultParamsWritable
- MLWritable
- HasProbabilityCol
- HasWeightCol
- HasLabelCol
- HasPredictionCol
- Evaluator
- Params
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Instance Constructors
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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @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 toparamMap. 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: AnyRef): 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
extractParamMapwith no extra values.extractParamMapwith 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
- def getBeta: Double
- Annotations
- @Since("3.0.0")
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @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
- @IntrinsicCandidate() @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
evaluateshould be maximized (true, default) or minimized (false).Indicates whether the metric returned by
evaluateshould 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
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @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("If the input path already exists but overwrite is not enabled.")
- 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.
- 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(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from HasProbabilityCol
Inherited from HasWeightCol
Inherited from HasLabelCol
Inherited from HasPredictionCol
Inherited from Evaluator
Inherited from Params
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from 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.