class ClassBalancer extends Estimator[ClassBalancerModel] with DefaultParamsWritable with HasInputCol with HasOutputCol with Wrappable with SynapseMLLogging
An estimator that calculates the weights for balancing a dataset. For example, if the negative class is half the size of the positive class, the weights will be 2 for rows with negative classes and 1 for rows with positive classes. these weights can be used in weighted classifiers and regressors to correct for heavily skewed datasets. The inputCol should be the labels of the classes, and the output col will be the requisite weights.
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def
clear(param: Param[_]): ClassBalancer.this.type
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def
companionModelClassName: String
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def
copy(extra: ParamMap): Estimator[ClassBalancerModel]
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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lazy val
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defaultCopy[T <: Params](extra: ParamMap): T
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dotnetClassNameString: String
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dotnetExtraEstimatorImports: String
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def
fit(dataset: Dataset[_]): ClassBalancerModel
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def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[ClassBalancerModel]
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def
fit(dataset: Dataset[_], paramMap: ParamMap): ClassBalancerModel
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def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ClassBalancerModel
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final
def
get[T](param: Param[T]): Option[T]
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def
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final
def
getDefault[T](param: Param[T]): Option[T]
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def
getInputCol: String
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final
def
getOrDefault[T](param: Param[T]): T
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def
getOutputCol: String
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def
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def
getPayload(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], exception: Option[Exception]): Map[String, String]
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def
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val
inputCol: Param[String]
The name of the input column
The name of the input column
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log: Logger
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def
logBase(info: Map[String, String], featureName: Option[String]): Unit
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logBase(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], featureName: Option[String]): Unit
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val
outputCol: Param[String]
The name of the output column
The name of the output column
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lazy val
params: Array[Param[_]]
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def
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pyClassName: String
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pyParamsDefaults: String
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pyParamsGetters: String
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rInternalWrapper: Boolean
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save(path: String): Unit
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final
def
set(paramPair: ParamPair[_]): ClassBalancer.this.type
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final
def
set(param: String, value: Any): ClassBalancer.this.type
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final
def
set[T](param: Param[T], value: T): ClassBalancer.this.type
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final
def
setDefault(paramPairs: ParamPair[_]*): ClassBalancer.this.type
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final
def
setDefault[T](param: Param[T], value: T): ClassBalancer.this.type
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- protected[org.apache.spark.ml]
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def
setInputCol(value: String): ClassBalancer.this.type
- Definition Classes
- HasInputCol
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def
setOutputCol(value: String): ClassBalancer.this.type
- Definition Classes
- HasOutputCol
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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val
thisStage: Params
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def
toString(): String
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def
transformSchema(schema: StructType): StructType
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- ClassBalancer → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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val
uid: String
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- ClassBalancer → SynapseMLLogging → Identifiable
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
write: MLWriter
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