org.apache.spark.ml.classification

LogisticRegression

class LogisticRegression extends Estimator[LogisticRegressionModel] with LogisticRegressionParams

Logistic regression.

Linear Supertypes
LogisticRegressionParams, HasPredictionCol, HasScoreCol, HasFeaturesCol, HasThreshold, HasLabelCol, HasMaxIter, HasRegParam, Estimator[LogisticRegressionModel], Params, Identifiable, PipelineStage, Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. LogisticRegression
  2. LogisticRegressionParams
  3. HasPredictionCol
  4. HasScoreCol
  5. HasFeaturesCol
  6. HasThreshold
  7. HasLabelCol
  8. HasMaxIter
  9. HasRegParam
  10. Estimator
  11. Params
  12. Identifiable
  13. PipelineStage
  14. Logging
  15. Serializable
  16. Serializable
  17. AnyRef
  18. Any
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Instance Constructors

  1. new LogisticRegression()

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. def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    parameter map

    returns

    fitted model

    Definition Classes
    LogisticRegressionEstimator
  14. def fit(dataset: JavaSchemaRDD, paramMaps: Array[ParamMap]): List[LogisticRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters.

    dataset

    input dataset

    paramMaps

    an array of parameter maps

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  15. def fit(dataset: JavaSchemaRDD, paramMap: ParamMap): LogisticRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    parameter map

    returns

    fitted model

    Definition Classes
    Estimator
  16. def fit(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    paramPairs

    optional list of param pairs (overwrite embedded params)

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  17. def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[LogisticRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could overwrite this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    an array of parameter maps

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  18. def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    paramPairs

    optional list of param pairs (overwrite embedded params)

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  19. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  20. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  21. def getLabelCol: String

    Definition Classes
    HasLabelCol
  22. def getMaxIter: Int

    Definition Classes
    HasMaxIter
  23. def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  24. def getRegParam: Double

    Definition Classes
    HasRegParam
  25. def getScoreCol: String

    Definition Classes
    HasScoreCol
  26. def getThreshold: Double

    Definition Classes
    HasThreshold
  27. def hashCode(): Int

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

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  30. def isTraceEnabled(): Boolean

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

    param for label column name

    param for label column name

    Definition Classes
    HasLabelCol
  32. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  39. def logName: String

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  44. val maxIter: IntParam

    param for max number of iterations

    param for max number of iterations

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

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

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

    Definition Classes
    AnyRef
  48. val paramMap: ParamMap

    Internal param map.

    Internal param map.

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

    Returns all params.

    Returns all params.

    Definition Classes
    Params
  50. val predictionCol: Param[String]

    param for prediction column name

    param for prediction column name

    Definition Classes
    HasPredictionCol
  51. val regParam: DoubleParam

    param for regularization parameter

    param for regularization parameter

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

    param for score column name

    param for score column name

    Definition Classes
    HasScoreCol
  53. def setFeaturesCol(value: String): LogisticRegression.this.type

  54. def setLabelCol(value: String): LogisticRegression.this.type

  55. def setMaxIter(value: Int): LogisticRegression.this.type

  56. def setPredictionCol(value: String): LogisticRegression.this.type

  57. def setRegParam(value: Double): LogisticRegression.this.type

  58. def setScoreCol(value: String): LogisticRegression.this.type

  59. def setThreshold(value: Double): LogisticRegression.this.type

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

    Definition Classes
    AnyRef
  61. val threshold: DoubleParam

    param for threshold in (binary) prediction

    param for threshold in (binary) prediction

    Definition Classes
    HasThreshold
  62. def toString(): String

    Definition Classes
    AnyRef → Any
  63. 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
  64. 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
  65. 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
  66. 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
  67. final def wait(): Unit

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  69. 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 Params

Inherited from Identifiable

Inherited from PipelineStage

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped