Packages

class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams with MLWritable

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Inherited
  1. LogisticRegressionModel
  2. MLWritable
  3. LogisticRegressionParams
  4. HasAggregationDepth
  5. HasThreshold
  6. HasWeightCol
  7. HasStandardization
  8. HasTol
  9. HasFitIntercept
  10. HasMaxIter
  11. HasElasticNetParam
  12. HasRegParam
  13. ProbabilisticClassificationModel
  14. ProbabilisticClassifierParams
  15. HasThresholds
  16. HasProbabilityCol
  17. ClassificationModel
  18. ClassifierParams
  19. HasRawPredictionCol
  20. PredictionModel
  21. PredictorParams
  22. HasPredictionCol
  23. HasFeaturesCol
  24. HasLabelCol
  25. Model
  26. Transformer
  27. PipelineStage
  28. Logging
  29. Params
  30. Serializable
  31. Serializable
  32. Identifiable
  33. AnyRef
  34. Any
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final val aggregationDepth: IntParam

    Param for suggested depth for treeAggregate (>= 2).

    Param for suggested depth for treeAggregate (>= 2).

    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def binarySummary: BinaryLogisticRegressionTrainingSummary

    Gets summary of model on training set.

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

    Annotations
    @Since( "2.3.0" )
  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.

    Definition Classes
    Params
  10. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  11. val coefficientMatrix: Matrix
    Annotations
    @Since( "2.1.0" )
  12. def coefficients: Vector

    A vector of model coefficients for "binomial" logistic regression.

    A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.

    returns

    Vector

    Annotations
    @Since( "2.0.0" )
  13. 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. See defaultCopy().

    Definition Classes
    LogisticRegressionModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "1.4.0" )
  14. 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
  15. 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
  16. 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
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  19. def evaluate(dataset: Dataset[_]): LogisticRegressionSummary

    Evaluates the model on a test dataset.

    Evaluates the model on a test dataset.

    dataset

    Test dataset to evaluate model on.

    Annotations
    @Since( "2.0.0" )
  20. 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
  21. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  22. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  23. 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.e., with ordering: default param values less than user-supplied values less than extra.

    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 less than user-supplied values less than extra.

    Definition Classes
    Params
  24. final val family: Param[String]

    Param for the name of family which is a description of the label distribution to be used in the model.

    Param for the name of family which is a description of the label distribution to be used in the model. Supported options:

    • "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
    • "binomial": Binary logistic regression with pivoting.
    • "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".
    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.1.0" )
  25. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  26. 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
  27. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  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 getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  31. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  32. 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
  33. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  34. def getFamily: String

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.1.0" )
  35. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  36. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  37. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  38. def getLowerBoundsOnCoefficients: Matrix

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  39. def getLowerBoundsOnIntercepts: Vector

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  40. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  41. 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
  42. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  43. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  44. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  45. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  46. final def getRegParam: Double

    Definition Classes
    HasRegParam
  47. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  48. def getThreshold: Double

    Get threshold for binary classification.

    Get threshold for binary classification.

    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 if set, or its default value if unset.

    1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold if set, or its default value if unset.

    Definition Classes
    LogisticRegressionModel → LogisticRegressionParams → HasThreshold
    Annotations
    @Since( "1.5.0" )
    Exceptions thrown

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

  49. 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
    Annotations
    @Since( "1.5.0" )
  50. final def getTol: Double

    Definition Classes
    HasTol
  51. def getUpperBoundsOnCoefficients: Matrix

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  52. def getUpperBoundsOnIntercepts: Vector

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  53. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  54. 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
  55. 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
  56. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  57. def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Annotations
    @Since( "1.5.0" )
  58. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  59. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  60. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def intercept: Double

    The model intercept for "binomial" logistic regression.

    The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.

    returns

    Double

    Annotations
    @Since( "1.3.0" )
  62. val interceptVector: Vector
    Annotations
    @Since( "2.1.0" )
  63. 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
  64. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  65. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  66. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  68. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  69. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  76. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. val lowerBoundsOnCoefficients: Param[Matrix]

    The lower bounds on coefficients if fitting under bound constrained optimization.

    The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  81. val lowerBoundsOnIntercepts: Param[Vector]

    The lower bounds on intercepts if fitting under bound constrained optimization.

    The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  82. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  83. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  84. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  86. val numClasses: Int

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

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

    Definition Classes
    LogisticRegressionModel → ClassificationModel
    Annotations
    @Since( "1.3.0" )
  87. val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    LogisticRegressionModel → PredictionModel
    Annotations
    @Since( "1.6.0" )
  88. 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.

    Definition Classes
    Params
    Note

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

  89. var parent: Estimator[LogisticRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  90. 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.

    Definition Classes
    LogisticRegressionModel → ClassificationModel → PredictionModel
  91. 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
  92. 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
    LogisticRegressionModel → ClassificationModel
  93. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  94. 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
    LogisticRegressionModel → ProbabilisticClassificationModel
  95. 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
  96. 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
    LogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel
  97. def raw2probability(rawPrediction: Vector): Vector

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  98. 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
    LogisticRegressionModel → ProbabilisticClassificationModel
  99. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

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

    Definition Classes
    HasRawPredictionCol
  100. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  101. 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( ... )
  102. 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
  103. 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
  104. 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.

    Definition Classes
    Params
  105. 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
  106. 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
  107. def setFeaturesCol(value: String): LogisticRegressionModel

    Definition Classes
    PredictionModel
  108. 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
  109. def setPredictionCol(value: String): LogisticRegressionModel

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

    Definition Classes
    ProbabilisticClassificationModel
  111. def setRawPredictionCol(value: String): LogisticRegressionModel

    Definition Classes
    ClassificationModel
  112. 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 greater than 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
    Annotations
    @Since( "1.5.0" )
  113. 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 greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's 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
    Annotations
    @Since( "1.5.0" )
  114. 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
  115. def summary: LogisticRegressionTrainingSummary

    Gets summary of model on training set.

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

    Annotations
    @Since( "1.5.0" )
  116. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  117. 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
  118. 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 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  119. def toString(): String
    Definition Classes
    LogisticRegressionModel → Identifiable → AnyRef → Any
  120. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  121. def transform(dataset: Dataset[_]): 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
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
  122. def transform(dataset: Dataset[_], 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
    Annotations
    @Since( "2.0.0" )
  123. def transform(dataset: Dataset[_], 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
    @Since( "2.0.0" ) @varargs()
  124. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  125. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    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.

    Definition Classes
    PredictionModel → PipelineStage
  126. 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()
  127. 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
    LogisticRegressionModel → Identifiable
    Annotations
    @Since( "1.4.0" )
  128. val upperBoundsOnCoefficients: Param[Matrix]

    The upper bounds on coefficients if fitting under bound constrained optimization.

    The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  129. val upperBoundsOnIntercepts: Param[Vector]

    The upper bounds on intercepts if fitting under bound constrained optimization.

    The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  130. def usingBoundConstrainedOptimization: Boolean
    Attributes
    protected
    Definition Classes
    LogisticRegressionParams
  131. 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., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  132. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  133. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  134. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  135. 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
  136. def write: MLWriter

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    For LogisticRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    This also does not save the parent currently.

    Definition Classes
    LogisticRegressionModel → MLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from MLWritable

Inherited from LogisticRegressionParams

Inherited from HasAggregationDepth

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 ProbabilisticClassificationModel[Vector, LogisticRegressionModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, LogisticRegressionModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, LogisticRegressionModel]

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

(expert-only) Parameters

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

(expert-only) Parameter getters