Packages

class GeneralizedLinearRegressionModel extends RegressionModel[Vector, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with MLWritable with HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]

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Inherited
  1. GeneralizedLinearRegressionModel
  2. HasTrainingSummary
  3. MLWritable
  4. GeneralizedLinearRegressionBase
  5. HasAggregationDepth
  6. HasSolver
  7. HasWeightCol
  8. HasRegParam
  9. HasTol
  10. HasMaxIter
  11. HasFitIntercept
  12. RegressionModel
  13. PredictionModel
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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Parameters

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

  1. final val family: Param[String]

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

    Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  2. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  3. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  4. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  5. final val link: Param[String]

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.

    Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  6. final val linkPower: DoubleParam

    Param for the index in the power link function.

    Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  7. final val linkPredictionCol: Param[String]

    Param for link prediction (linear predictor) column name.

    Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  8. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  9. final val offsetCol: Param[String]

    Param for offset column name.

    Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  10. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  11. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  12. final val solver: Param[String]

    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"

    Definition Classes
    GeneralizedLinearRegressionBase → HasSolver
    Annotations
    @Since( "2.0.0" )
  13. 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
  14. final val variancePower: DoubleParam

    Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.

    Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  15. 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

Members

  1. final def clear(param: Param[_]): GeneralizedLinearRegressionModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. val coefficients: Vector
    Annotations
    @Since( "2.0.0" )
  3. def copy(extra: ParamMap): GeneralizedLinearRegressionModel

    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
    GeneralizedLinearRegressionModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "2.0.0" )
  4. def evaluate(dataset: Dataset[_]): GeneralizedLinearRegressionSummary

    Evaluate the model on the given dataset, returning a summary of the results.

    Evaluate the model on the given dataset, returning a summary of the results.

    Annotations
    @Since( "2.0.0" )
  5. 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
  6. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  8. 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
  9. 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
  10. 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
  11. 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
  12. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  13. 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
  14. 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
  15. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  16. def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Definition Classes
    HasTrainingSummary
    Annotations
    @Since( "3.0.0" )
  17. val intercept: Double
    Annotations
    @Since( "2.0.0" )
  18. 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
  19. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  20. 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
    GeneralizedLinearRegressionModel → PredictionModel
  21. 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.

  22. var parent: Estimator[GeneralizedLinearRegressionModel]

    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.

  23. def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    Definition Classes
    GeneralizedLinearRegressionModel → PredictionModel
  24. 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( ... )
  25. final def set[T](param: Param[T], value: T): GeneralizedLinearRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  26. def setParent(parent: Estimator[GeneralizedLinearRegressionModel]): GeneralizedLinearRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  27. def summary: GeneralizedLinearRegressionTrainingSummary

    Gets R-like summary of model on training set.

    Gets R-like summary of model on training set. An exception is thrown if there is no summary available.

    Definition Classes
    GeneralizedLinearRegressionModel → HasTrainingSummary
    Annotations
    @Since( "2.0.0" )
  28. def toString(): String
    Definition Classes
    GeneralizedLinearRegressionModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  29. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    GeneralizedLinearRegressionModel → PredictionModel → Transformer
  30. 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" )
  31. 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()
  32. def transformSchema(schema: StructType): StructType

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

    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
  33. 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
    GeneralizedLinearRegressionModel → Identifiable
    Annotations
    @Since( "2.0.0" )
  34. 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

    Definition Classes
    GeneralizedLinearRegressionBase → PredictorParams
    Annotations
    @Since( "2.0.0" )
  35. 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 GeneralizedLinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

    Definition Classes
    GeneralizedLinearRegressionModel → MLWritable
    Annotations
    @Since( "2.0.0" )

Parameter setters

  1. def setFeaturesCol(value: String): GeneralizedLinearRegressionModel

    Definition Classes
    PredictionModel
  2. def setLinkPredictionCol(value: String): GeneralizedLinearRegressionModel.this.type

    Sets the link prediction (linear predictor) column name.

    Sets the link prediction (linear predictor) column name.

    Annotations
    @Since( "2.0.0" )
  3. def setPredictionCol(value: String): GeneralizedLinearRegressionModel

    Definition Classes
    PredictionModel

Parameter getters

  1. def getFamily: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  2. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  3. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  4. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  5. def getLink: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  6. def getLinkPower: Double

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  7. def getLinkPredictionCol: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  8. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  9. def getOffsetCol: String

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  10. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  11. final def getRegParam: Double

    Definition Classes
    HasRegParam
  12. final def getSolver: String

    Definition Classes
    HasSolver
  13. final def getTol: Double

    Definition Classes
    HasTol
  14. def getVariancePower: Double

    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  15. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(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.

  1. final val aggregationDepth: IntParam

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

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

    Definition Classes
    HasAggregationDepth

(expert-only) Parameter getters

  1. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth