All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, ClassifierParams, ClassifierTypeTrait, OneVsRestParams, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasRawPredictionCol, HasWeightCol, PredictorParams, Identifiable, MLWritable

Model produced by OneVsRest. This stores the models resulting from training k binary classifiers: one for each class. Each example is scored against all k models, and the model with the highest score is picked to label the example.

param: labelMetadata Metadata of label column if it exists, or Nominal attribute representing the number of classes in training dataset otherwise. param: models The binary classification models for the reduction. The i-th model is produced by testing the i-th class (taking label 1) vs the rest (taking label 0).

See Also:

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Method Summary

    param for the base binary classifier that we reduce multiclass classification into.

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

    Param for features column name.

    labelCol()

    Param for label column name.

    models()

    int

    int

    Param for prediction column name.

    Param for raw prediction (a.k.a.

    read()

    toString()

    Transforms the input dataset.

    Check transform validity and derive the output schema from the input schema.

    uid()

    An immutable unique ID for the object and its derivatives.

    weightCol()

    Param for weight column name.

    write()

    Returns an MLWriter instance for this ML instance.

    Methods inherited from interface org.apache.spark.internal.Logging

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

    Methods inherited from interface org.apache.spark.ml.util.MLWritable

    save

  • Method Details

    • read

    • load

    • classifier

      param for the base binary classifier that we reduce multiclass classification into. The base classifier input and output columns are ignored in favor of the ones specified in OneVsRest.

      Specified by:
      classifier in interface OneVsRestParams
      Returns:
      (undocumented)

    • weightCol

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

      Specified by:
      weightCol in interface HasWeightCol
      Returns:
      (undocumented)

    • rawPredictionCol

      public final Param<String> rawPredictionCol()

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

      Specified by:
      rawPredictionCol in interface HasRawPredictionCol
      Returns:
      (undocumented)

    • predictionCol

      Param for prediction column name.

      Specified by:
      predictionCol in interface HasPredictionCol
      Returns:
      (undocumented)

    • featuresCol

      Param for features column name.

      Specified by:
      featuresCol in interface HasFeaturesCol
      Returns:
      (undocumented)

    • labelCol

      Description copied from interface: HasLabelCol

      Param for label column name.

      Specified by:
      labelCol in interface HasLabelCol
      Returns:
      (undocumented)

    • uid

      An immutable unique ID for the object and its derivatives.

      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • models

    • numClasses

      public int numClasses()

    • numFeatures

      public int numFeatures()

    • setFeaturesCol

    • setPredictionCol

    • setRawPredictionCol

    • transformSchema

      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)

    • transform

      Transforms the input dataset.

      Specified by:
      transform in class Transformer
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • copy

      Description copied from interface: 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().

      Specified by:
      copy in interface Params
      Specified by:
      copy in class Model<OneVsRestModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      Description copied from interface: MLWritable

      Returns an MLWriter instance for this ML instance.

      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object