Class/Object

org.apache.spark.ml.classification

LogisticRegressionModel

Related Docs: object LogisticRegressionModel | package classification

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

Model produced by LogisticRegression.

Annotations
@Since( "1.4.0" )
Source
LogisticRegression.scala
Linear Supertypes
MLWritable, LogisticRegressionParams, HasThreshold, HasWeightCol, 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
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Inherited
  1. LogisticRegressionModel
  2. MLWritable
  3. LogisticRegressionParams
  4. HasThreshold
  5. HasWeightCol
  6. HasStandardization
  7. HasTol
  8. HasFitIntercept
  9. HasMaxIter
  10. HasElasticNetParam
  11. HasRegParam
  12. ProbabilisticClassificationModel
  13. ProbabilisticClassifierParams
  14. HasThresholds
  15. HasProbabilityCol
  16. ClassificationModel
  17. ClassifierParams
  18. HasRawPredictionCol
  19. PredictionModel
  20. PredictorParams
  21. HasPredictionCol
  22. HasFeaturesCol
  23. HasLabelCol
  24. Model
  25. Transformer
  26. PipelineStage
  27. Logging
  28. Params
  29. Serializable
  30. Serializable
  31. Identifiable
  32. AnyRef
  33. Any
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Visibility
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Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def checkThresholdConsistency(): Unit

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    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

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

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val coefficients: Vector

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    Annotations
    @Since( "2.0.0" )
  10. def copy(extra: ParamMap): LogisticRegressionModel

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    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
    LogisticRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  13. final val elasticNetParam: DoubleParam

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    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
  14. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  16. def evaluate(dataset: Dataset[_]): LogisticRegressionSummary

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    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" )
  17. def explainParam(param: Param[_]): String

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    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

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    Explains all params of this instance.

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

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

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. final def extractParamMap(extra: ParamMap): ParamMap

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    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
  21. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  22. def featuresDataType: DataType

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    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

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

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    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]

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    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[_]

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    Definition Classes
    AnyRef → Any
  27. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  28. final def getElasticNetParam: Double

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    Definition Classes
    HasElasticNetParam
  29. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  30. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  31. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  32. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  33. final def getOrDefault[T](param: Param[T]): T

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    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]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  35. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  36. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  37. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  38. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  39. final def getStandardization: Boolean

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    Definition Classes
    HasStandardization
  40. def getThreshold: Double

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    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.

  41. def getThresholds: Array[Double]

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    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" )
  42. final def getTol: Double

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    Definition Classes
    HasTol
  43. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  44. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  45. def hasParam(paramName: String): Boolean

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    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
  46. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  47. def hasSummary: Boolean

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    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" )
  48. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  49. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  50. val intercept: Double

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    Annotations
    @Since( "1.3.0" )
  51. final def isDefined(param: Param[_]): Boolean

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    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

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

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean

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

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  56. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  63. def logName: String

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

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

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

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

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

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

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

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

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

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

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    Definition Classes
    AnyRef
  72. val numClasses: Int

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

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

    Definition Classes
    LogisticRegressionModelClassificationModel
    Annotations
    @Since( "1.3.0" )
  73. val numFeatures: Int

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    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
    LogisticRegressionModelPredictionModel
    Annotations
    @Since( "1.6.0" )
  74. lazy val params: Array[Param[_]]

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    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
  75. var parent: Estimator[LogisticRegressionModel]

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    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
  76. def predict(features: Vector): Double

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    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
  77. def predictProbability(features: Vector): Vector

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    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
  78. def predictRaw(features: Vector): Vector

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    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
  79. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

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

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    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
  81. final val probabilityCol: Param[String]

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    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
  82. def raw2prediction(rawPrediction: Vector): Double

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    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
  83. def raw2probability(rawPrediction: Vector): Vector

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    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

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

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    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
  85. final val rawPredictionCol: Param[String]

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    Param for raw prediction (a.k.a.

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

    Definition Classes
    HasRawPredictionCol
  86. final val regParam: DoubleParam

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    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

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

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    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( ... )
  88. final def set(paramPair: ParamPair[_]): LogisticRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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
  90. final def set[T](param: Param[T], value: T): LogisticRegressionModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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
  92. final def setDefault[T](param: Param[T], value: T): LogisticRegressionModel.this.type

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    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
  93. def setFeaturesCol(value: String): LogisticRegressionModel

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

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

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

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

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  97. def setRawPredictionCol(value: String): LogisticRegressionModel

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

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    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
    Annotations
    @Since( "1.5.0" )
  99. def setThresholds(value: Array[Double]): LogisticRegressionModel.this.type

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    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
    Annotations
    @Since( "1.5.0" )
  100. final val standardization: BooleanParam

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    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
  101. def summary: LogisticRegressionTrainingSummary

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

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    Definition Classes
    AnyRef
  103. final val threshold: DoubleParam

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    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
  104. final val thresholds: DoubleArrayParam

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    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
  105. def toString(): String

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

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    Param for the convergence tolerance for iterative algorithms.

    Param for the convergence tolerance for iterative algorithms.

    Definition Classes
    HasTol
  107. def transform(dataset: Dataset[_]): DataFrame

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    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
  108. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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

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    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()
  110. def transformImpl(dataset: Dataset[_]): DataFrame

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

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    :: DeveloperApi ::

    :: DeveloperApi ::

    Check transform validity and derive the output schema from the input schema.

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

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

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    :: 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()
  113. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    LogisticRegressionModelIdentifiable
    Annotations
    @Since( "1.4.0" )
  114. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    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
  115. final def wait(): Unit

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

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

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

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    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
  119. def write: MLWriter

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    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
    LogisticRegressionModelMLWritable
    Annotations
    @Since( "1.6.0" )

Deprecated Value Members

  1. def validateParams(): Unit

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    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
    Deprecated

    Will be removed in 2.1.0. All the checks should be merged into transformSchema

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 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