org.apache.spark.ml.classification

LogisticRegression

class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

:: Experimental :: Logistic regression. Currently, this class only supports binary classification. It will support multiclass in the future.

Annotations
@Since( "1.2.0" ) @Experimental()
Source
LogisticRegression.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, LogisticRegressionParams, HasThreshold, HasWeightCol, HasStandardization, HasTol, HasFitIntercept, HasMaxIter, HasElasticNetParam, HasRegParam, ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, LogisticRegression, LogisticRegressionModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, LogisticRegression, LogisticRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[LogisticRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LogisticRegression
  2. DefaultParamsWritable
  3. MLWritable
  4. LogisticRegressionParams
  5. HasThreshold
  6. HasWeightCol
  7. HasStandardization
  8. HasTol
  9. HasFitIntercept
  10. HasMaxIter
  11. HasElasticNetParam
  12. HasRegParam
  13. ProbabilisticClassifier
  14. ProbabilisticClassifierParams
  15. HasThresholds
  16. HasProbabilityCol
  17. Classifier
  18. ClassifierParams
  19. HasRawPredictionCol
  20. Predictor
  21. PredictorParams
  22. HasPredictionCol
  23. HasFeaturesCol
  24. HasLabelCol
  25. Estimator
  26. PipelineStage
  27. Logging
  28. Params
  29. Serializable
  30. Serializable
  31. Identifiable
  32. AnyRef
  33. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LogisticRegression()

    Annotations
    @Since( "1.4.0" )
  2. new LogisticRegression(uid: String)

    Annotations
    @Since( "1.2.0" )

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[_]): LogisticRegression.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  10. def clone(): AnyRef

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

    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
    LogisticRegressionPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.0" )
    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. def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  20. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fit(dataset: DataFrame): LogisticRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  25. def fit(dataset: DataFrame, 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 override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  26. def fit(dataset: DataFrame, 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. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
  27. def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: 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

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  28. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  29. 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
  30. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  31. 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
  32. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  33. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  34. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  35. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  36. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  37. 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
  38. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  39. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  40. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  41. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  42. final def getRegParam: Double

    Definition Classes
    HasRegParam
  43. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  44. 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
    LogisticRegression → LogisticRegressionParams → HasThreshold
    Annotations
    @Since( "1.5.0" )
    Exceptions thrown
    IllegalArgumentException

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

  45. 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
    LogisticRegression → LogisticRegressionParams → HasThresholds
    Annotations
    @Since( "1.5.0" )
  46. final def getTol: Double

    Definition Classes
    HasTol
  47. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  48. 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
  49. 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
  50. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  51. 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
  52. final def isInstanceOf[T0]: Boolean

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  56. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  63. def logName: String

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

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

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

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

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

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

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

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

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

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

    Definition Classes
    AnyRef
  72. 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
  73. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  74. 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
  75. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.

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

    Definition Classes
    HasRawPredictionCol
  76. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  77. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  78. final def set(paramPair: ParamPair[_]): LogisticRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  79. final def set(param: String, value: Any): LogisticRegression.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
  80. final def set[T](param: Param[T], value: T): LogisticRegression.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  81. final def setDefault(paramPairs: ParamPair[_]*): LogisticRegression.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
  82. final def setDefault[T](param: Param[T], value: T): LogisticRegression.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
  83. def setElasticNetParam(value: Double): LogisticRegression.this.type

    Set the ElasticNet mixing parameter.

    Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

    Annotations
    @Since( "1.4.0" )
  84. def setFeaturesCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  85. def setFitIntercept(value: Boolean): LogisticRegression.this.type

    Whether to fit an intercept term.

    Whether to fit an intercept term. Default is true.

    Annotations
    @Since( "1.4.0" )
  86. def setLabelCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  87. def setMaxIter(value: Int): LogisticRegression.this.type

    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations
    @Since( "1.2.0" )
  88. def setPredictionCol(value: String): LogisticRegression

    Definition Classes
    Predictor
  89. def setProbabilityCol(value: String): LogisticRegression

    Definition Classes
    ProbabilisticClassifier
  90. def setRawPredictionCol(value: String): LogisticRegression

    Definition Classes
    Classifier
  91. def setRegParam(value: Double): LogisticRegression.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

    Annotations
    @Since( "1.2.0" )
  92. def setStandardization(value: Boolean): LogisticRegression.this.type

    Whether to standardize the training features before fitting the model.

    Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

    Annotations
    @Since( "1.5.0" )
  93. def setThreshold(value: Double): LogisticRegression.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
    LogisticRegression → LogisticRegressionParams
    Annotations
    @Since( "1.5.0" )
  94. def setThresholds(value: Array[Double]): LogisticRegression.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
    LogisticRegression → LogisticRegressionParams → ProbabilisticClassifier
    Annotations
    @Since( "1.5.0" )
  95. def setTol(value: Double): LogisticRegression.this.type

    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

    Annotations
    @Since( "1.4.0" )
  96. def setWeightCol(value: String): LogisticRegression.this.type

    Whether to over-/under-sample training instances according to the given weights in weightCol.

    Whether to over-/under-sample training instances according to the given weights in weightCol. If empty, all instances are treated equally (weight 1.0). Default is empty, so all instances have weight one.

    Annotations
    @Since( "1.6.0" )
  97. 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
  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 train(dataset: DataFrame): LogisticRegressionModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    LogisticRegressionPredictor
  104. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictorPipelineStage
  105. 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()
  106. 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
    LogisticRegressionIdentifiable
    Annotations
    @Since( "1.4.0" )
  107. 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
  108. 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
  109. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. final val weightCol: Param[String]

    Param for weight column name.

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

    Definition Classes
    HasWeightCol
  113. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from LogisticRegressionParams

Inherited from HasThreshold

Inherited from HasWeightCol

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 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