org.apache.spark.ml.classification

LogisticRegressionModel

class LogisticRegressionModel extends Model[LogisticRegressionModel] with LogisticRegressionParams

:: AlphaComponent :: Model produced by LogisticRegression.

Annotations
@AlphaComponent()
Linear Supertypes
LogisticRegressionParams, HasPredictionCol, HasScoreCol, HasFeaturesCol, HasThreshold, HasLabelCol, HasMaxIter, HasRegParam, Model[LogisticRegressionModel], Transformer, Params, Identifiable, PipelineStage, Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. LogisticRegressionModel
  2. LogisticRegressionParams
  3. HasPredictionCol
  4. HasScoreCol
  5. HasFeaturesCol
  6. HasThreshold
  7. HasLabelCol
  8. HasMaxIter
  9. HasRegParam
  10. Model
  11. Transformer
  12. Params
  13. Identifiable
  14. PipelineStage
  15. Logging
  16. Serializable
  17. Serializable
  18. AnyRef
  19. Any
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Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

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

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

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef → Any
  10. def explainParams(): String

    Returns the documentation of all params.

    Returns the documentation of all params.

    Definition Classes
    Params
  11. val featuresCol: Param[String]

    param for features column name

    param for features column name

    Definition Classes
    HasFeaturesCol
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. val fittingParamMap: ParamMap

    Fitting parameters, such that parent.

    Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model.

    Definition Classes
    LogisticRegressionModelModel
  14. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  15. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  16. def getLabelCol: String

    Definition Classes
    HasLabelCol
  17. def getMaxIter: Int

    Definition Classes
    HasMaxIter
  18. def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  19. def getRegParam: Double

    Definition Classes
    HasRegParam
  20. def getScoreCol: String

    Definition Classes
    HasScoreCol
  21. def getThreshold: Double

    Definition Classes
    HasThreshold
  22. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  23. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  24. def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  25. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  26. val labelCol: Param[String]

    param for label column name

    param for label column name

    Definition Classes
    HasLabelCol
  27. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  28. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  29. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  30. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  31. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  32. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  33. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  34. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  35. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  36. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  37. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  38. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  39. val maxIter: IntParam

    param for max number of iterations

    param for max number of iterations

    Definition Classes
    HasMaxIter
  40. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  41. final def notify(): Unit

    Definition Classes
    AnyRef
  42. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  43. val paramMap: ParamMap

    Internal param map.

    Internal param map.

    Attributes
    protected
    Definition Classes
    Params
  44. def params: Array[Param[_]]

    Returns all params.

    Returns all params.

    Definition Classes
    Params
  45. val parent: LogisticRegression

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    LogisticRegressionModelModel
  46. val predictionCol: Param[String]

    param for prediction column name

    param for prediction column name

    Definition Classes
    HasPredictionCol
  47. val regParam: DoubleParam

    param for regularization parameter

    param for regularization parameter

    Definition Classes
    HasRegParam
  48. val scoreCol: Param[String]

    param for score column name

    param for score column name

    Definition Classes
    HasScoreCol
  49. def setFeaturesCol(value: String): LogisticRegressionModel.this.type

  50. def setPredictionCol(value: String): LogisticRegressionModel.this.type

  51. def setScoreCol(value: String): LogisticRegressionModel.this.type

  52. def setThreshold(value: Double): LogisticRegressionModel.this.type

  53. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  54. val threshold: DoubleParam

    param for threshold in (binary) prediction

    param for threshold in (binary) prediction

    Definition Classes
    HasThreshold
  55. def toString(): String

    Definition Classes
    AnyRef → Any
  56. def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    LogisticRegressionModelTransformer
  57. def transform(dataset: JavaSchemaRDD, paramMap: ParamMap): JavaSchemaRDD

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
  58. def transform(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): JavaSchemaRDD

    Transforms the dataset with optional parameters.

    Transforms the dataset with optional parameters.

    dataset

    input datset

    paramPairs

    optional list of param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @varargs()
  59. def transform(dataset: SchemaRDD, paramPairs: ParamPair[_]*): SchemaRDD

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    paramPairs

    optional list of param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @varargs()
  60. def transformSchema(schema: StructType, paramMap: ParamMap, logging: Boolean): StructType

    Derives the output schema from the input schema and parameters, optionally with logging.

    Derives the output schema from the input schema and parameters, optionally with logging.

    Attributes
    protected
    Definition Classes
    PipelineStage
  61. def validate(): Unit

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    Definition Classes
    Params
  62. def validate(paramMap: ParamMap): Unit

    Validates parameter values stored internally plus the input parameter map.

    Validates parameter values stored internally plus the input parameter map. Raises an exception if any parameter is invalid.

    Definition Classes
    Params
  63. def validateAndTransformSchema(schema: StructType, paramMap: ParamMap, fitting: Boolean): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    paramMap

    additional parameters

    fitting

    whether this is in fitting

    returns

    output schema

    Attributes
    protected
    Definition Classes
    LogisticRegressionParams
  64. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from LogisticRegressionParams

Inherited from HasPredictionCol

Inherited from HasScoreCol

Inherited from HasFeaturesCol

Inherited from HasThreshold

Inherited from HasLabelCol

Inherited from HasMaxIter

Inherited from HasRegParam

Inherited from Model[LogisticRegressionModel]

Inherited from Transformer

Inherited from Params

Inherited from Identifiable

Inherited from PipelineStage

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped