org.apache.spark.ml.classification

LogisticRegressionModel

class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams

:: Experimental :: Model produced by LogisticRegression.

Annotations
@Experimental()
Linear Supertypes
LogisticRegressionParams, HasThreshold, HasStandardization, HasTol, HasFitIntercept, HasMaxIter, HasElasticNetParam, HasRegParam, ProbabilisticClassificationModel[Vector, LogisticRegressionModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, LogisticRegressionModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, LogisticRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[LogisticRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By inheritance
Inherited
  1. LogisticRegressionModel
  2. LogisticRegressionParams
  3. HasThreshold
  4. HasStandardization
  5. HasTol
  6. HasFitIntercept
  7. HasMaxIter
  8. HasElasticNetParam
  9. HasRegParam
  10. ProbabilisticClassificationModel
  11. ProbabilisticClassifierParams
  12. HasThresholds
  13. HasProbabilityCol
  14. ClassificationModel
  15. ClassifierParams
  16. HasRawPredictionCol
  17. PredictionModel
  18. PredictorParams
  19. HasPredictionCol
  20. HasFeaturesCol
  21. HasLabelCol
  22. Model
  23. Transformer
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Serializable
  29. Identifiable
  30. AnyRef
  31. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

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 $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  5. final def ==(arg0: AnyRef): Boolean

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

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

    Definition Classes
    Any
  8. def checkThresholdConsistency(): Unit

    If threshold and thresholds are both set, ensures they are consistent.

    If threshold and thresholds are both set, ensures they are consistent.

    Attributes
    protected
    Definition Classes
    LogisticRegressionParams
    Exceptions thrown
    IllegalArgumentException

    if threshold and thresholds are not equivalent

  9. final def clear(param: Param[_]): LogisticRegressionModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Attributes
    protected
    Definition Classes
    Params
  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. def copy(extra: ParamMap): LogisticRegressionModel

    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.

    Definition Classes
    LogisticRegressionModelModelTransformerPipelineStageParams
    See also

    defaultCopy()

  12. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  13. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  14. final val elasticNetParam: DoubleParam

    Param for the ElasticNet mixing parameter, in range [0, 1].

    Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty..

    Definition Classes
    HasElasticNetParam
  15. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  17. 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
  18. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  19. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. 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.

    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 < user-supplied values < extra.

    Definition Classes
    Params
  21. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  25. 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
  26. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  27. 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
  28. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  29. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  30. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  31. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  32. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  33. 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
  34. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  35. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  36. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  37. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  38. final def getRegParam: Double

    Definition Classes
    HasRegParam
  39. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  40. def getThreshold: Double

    Get threshold for binary classification.

    Get threshold for binary classification.

    If threshold is set, returns that value. Otherwise, if thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:

    1 / (1 + thresholds(0) / thresholds(1))

    . Otherwise, returns threshold default value.

    1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold default value.

    Definition Classes
    LogisticRegressionModel → LogisticRegressionParams → HasThreshold
    Exceptions thrown
    IllegalArgumentException

    if thresholds is set to an array of length other than 2.

  41. def getThresholds: Array[Double]

    Get thresholds for binary or multiclass classification.

    Get thresholds for binary or multiclass classification.

    If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

    Definition Classes
    LogisticRegressionModel → LogisticRegressionParams → HasThresholds
  42. final def getTol: Double

    Definition Classes
    HasTol
  43. 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
  44. 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
  45. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  46. def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

  47. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  48. val intercept: Double

  49. 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
  50. final def isInstanceOf[T0]: Boolean

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  52. def isTraceEnabled(): Boolean

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  54. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  61. def logName: String

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  66. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

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

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

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

    Definition Classes
    AnyRef
  70. val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes
    LogisticRegressionModelClassificationModel
  71. 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.

    Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

    Definition Classes
    Params
  72. var parent: Estimator[LogisticRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

    Definition Classes
    Model
  73. def predict(features: Vector): Double

    Predict label for the given feature vector.

    Predict label for the given feature vector. The behavior of this can be adjusted using thresholds.

    Attributes
    protected
    Definition Classes
    LogisticRegressionModelClassificationModelPredictionModel
  74. def predictProbability(features: Vector): Vector

    Predict the probability of each class given the features.

    Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  75. def predictRaw(features: Vector): Vector

    Raw prediction for each possible label.

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

    returns

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    Attributes
    protected
    Definition Classes
    LogisticRegressionModelClassificationModel
  76. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  77. def probability2prediction(probability: Vector): Double

    Given a vector of class conditional probabilities, select the predicted label.

    Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    LogisticRegressionModelProbabilisticClassificationModel
  78. final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities..

    Definition Classes
    HasProbabilityCol
  79. def raw2prediction(rawPrediction: Vector): Double

    Given a vector of raw predictions, select the predicted label.

    Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    LogisticRegressionModelProbabilisticClassificationModelClassificationModel
  80. def raw2probability(rawPrediction: Vector): Vector

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  81. def raw2probabilityInPlace(rawPrediction: Vector): Vector

    Estimate the probability of each class given the raw prediction, doing the computation in-place.

    Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities (modified input vector)

    Attributes
    protected
    Definition Classes
    LogisticRegressionModelProbabilisticClassificationModel
  82. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.

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

    Definition Classes
    HasRawPredictionCol
  83. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  84. final def set(paramPair: ParamPair[_]): LogisticRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  85. final def set(param: String, value: Any): LogisticRegressionModel.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  86. final def set[T](param: Param[T], value: T): LogisticRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  87. final def setDefault(paramPairs: ParamPair[_]*): LogisticRegressionModel.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault(). Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  88. final def setDefault[T](param: Param[T], value: T): LogisticRegressionModel.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  89. def setFeaturesCol(value: String): LogisticRegressionModel

    Definition Classes
    PredictionModel
  90. def setParent(parent: Estimator[LogisticRegressionModel]): LogisticRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  91. def setPredictionCol(value: String): LogisticRegressionModel

    Definition Classes
    PredictionModel
  92. def setProbabilityCol(value: String): LogisticRegressionModel

  93. def setRawPredictionCol(value: String): LogisticRegressionModel

    Definition Classes
    ClassificationModel
  94. def setThreshold(value: Double): LogisticRegressionModel.this.type

    Set threshold in binary classification, in range [0, 1].

    Set threshold in binary classification, in range [0, 1].

    If the estimated probability of class label 1 is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.

    Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

    Default is 0.5.

    Definition Classes
    LogisticRegressionModel → LogisticRegressionParams
  95. def setThresholds(value: Array[Double]): LogisticRegressionModel.this.type

    Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.

    Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.

    Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

    Definition Classes
    LogisticRegressionModel → LogisticRegressionParams → ProbabilisticClassificationModel
  96. final val standardization: BooleanParam

    Param for whether to standardize the training features before fitting the model.

    Param for whether to standardize the training features before fitting the model..

    Definition Classes
    HasStandardization
  97. def summary: LogisticRegressionTrainingSummary

    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if trainingSummary == None.

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

    Definition Classes
    AnyRef
  99. final val threshold: DoubleParam

    Param for threshold in binary classification prediction, in range [0, 1].

    Param for threshold in binary classification prediction, in range [0, 1].

    Definition Classes
    HasThreshold
  100. final val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..

    Definition Classes
    HasThresholds
  101. def toString(): String

    Definition Classes
    Identifiable → AnyRef → Any
  102. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms.

    Param for the convergence tolerance for iterative algorithms.

    Definition Classes
    HasTol
  103. def transform(dataset: DataFrame): DataFrame

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    ProbabilisticClassificationModelClassificationModelPredictionModelTransformer
  104. def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

    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
  105. def transform(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @varargs()
  106. def transformImpl(dataset: DataFrame): DataFrame

    Attributes
    protected
    Definition Classes
    PredictionModel
  107. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictionModelPipelineStage
  108. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

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

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  109. 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
    LogisticRegressionModelIdentifiable
  110. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): 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

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  111. def validateParams(): Unit

    Validates parameter values stored internally.

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

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    LogisticRegressionParams → Params
  112. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  115. val weights: Vector

Inherited from LogisticRegressionParams

Inherited from HasThreshold

Inherited from HasStandardization

Inherited from HasTol

Inherited from HasFitIntercept

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[LogisticRegressionModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters