org.apache.spark.ml.evaluation
BinaryClassificationEvaluator
Companion object BinaryClassificationEvaluator
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
- Grouped
- Alphabetic
- By Inheritance
- BinaryClassificationEvaluator
- DefaultParamsWritable
- MLWritable
- HasWeightCol
- HasLabelCol
- HasRawPredictionCol
- Evaluator
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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- 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.
-
final
val
labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
-
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" )
-
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
-
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
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
-
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
- BinaryClassificationEvaluator → Evaluator → Params
- Annotations
- @Since( "1.4.1" )
-
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
- BinaryClassificationEvaluator → 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
-
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
-
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" )
-
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
-
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
- BinaryClassificationEvaluator → 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
-
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[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
-
def
toString(): String
- Definition Classes
- BinaryClassificationEvaluator → 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
- BinaryClassificationEvaluator → Identifiable
- Annotations
- @Since( "1.4.0" )
-
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
setLabelCol(value: String): BinaryClassificationEvaluator.this.type
- Annotations
- @Since( "1.2.0" )
-
def
setMetricName(value: String): BinaryClassificationEvaluator.this.type
- Annotations
- @Since( "1.2.0" )
-
def
setRawPredictionCol(value: String): BinaryClassificationEvaluator.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setWeightCol(value: String): BinaryClassificationEvaluator.this.type
- Annotations
- @Since( "3.0.0" )
Parameter getters
-
final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
-
def
getMetricName: String
- Annotations
- @Since( "1.2.0" )
-
final
def
getRawPredictionCol: String
- Definition Classes
- HasRawPredictionCol
-
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.
(expert-only) Parameter setters
-
def
setNumBins(value: Int): BinaryClassificationEvaluator.this.type
- Annotations
- @Since( "3.0.0" )
(expert-only) Parameter getters
-
def
getNumBins: Int
- Annotations
- @Since( "3.0.0" )