Packages

class FMClassificationModel extends ProbabilisticClassificationModel[Vector, FMClassificationModel] with FMClassifierParams with MLWritable

Model produced by FMClassifier

Annotations
@Since( "3.0.0" )
Source
FMClassifier.scala
Linear Supertypes
MLWritable, FMClassifierParams, FactorizationMachinesParams, HasRegParam, HasFitIntercept, HasSeed, HasSolver, HasTol, HasStepSize, HasMaxIter, ProbabilisticClassificationModel[Vector, FMClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, FMClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, FMClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[FMClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. FMClassificationModel
  2. MLWritable
  3. FMClassifierParams
  4. FactorizationMachinesParams
  5. HasRegParam
  6. HasFitIntercept
  7. HasSeed
  8. HasSolver
  9. HasTol
  10. HasStepSize
  11. HasMaxIter
  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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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 def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): FMClassificationModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copy(extra: ParamMap): FMClassificationModel

    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
    FMClassificationModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "3.0.0" )
  9. 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
  10. 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
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. 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
  14. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  15. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  16. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  17. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  18. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  19. 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
  20. final val factorSize: IntParam

    Param for dimensionality of the factors (>= 0)

    Param for dimensionality of the factors (>= 0)

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  21. val factors: Matrix
    Annotations
    @Since( "3.0.0" )
  22. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. 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
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  26. final val fitLinear: BooleanParam

    Param for whether to fit linear term (aka 1-way term)

    Param for whether to fit linear term (aka 1-way term)

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  27. 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
  28. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  29. 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
  30. final def getFactorSize: Int

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  31. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  32. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  33. final def getFitLinear: Boolean

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  34. final def getInitStd: Double

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  35. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  36. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  37. final def getMiniBatchFraction: Double

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  38. 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
  39. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  40. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  41. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  42. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  43. final def getRegParam: Double

    Definition Classes
    HasRegParam
  44. final def getSeed: Long

    Definition Classes
    HasSeed
  45. final def getSolver: String

    Definition Classes
    HasSolver
  46. final def getStepSize: Double

    Definition Classes
    HasStepSize
  47. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  48. final def getTol: Double

    Definition Classes
    HasTol
  49. 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
  50. 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
  51. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  52. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. final val initStd: DoubleParam

    Param for standard deviation of initial coefficients

    Param for standard deviation of initial coefficients

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  54. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  55. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. val intercept: Double
    Annotations
    @Since( "3.0.0" )
  57. 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
  58. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  59. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  62. val linear: Vector
    Annotations
    @Since( "3.0.0" )
  63. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  76. final val miniBatchFraction: DoubleParam

    Param for mini-batch fraction, must be in range (0, 1]

    Param for mini-batch fraction, must be in range (0, 1]

    Definition Classes
    FactorizationMachinesParams
    Annotations
    @Since( "3.0.0" )
  77. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  78. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  79. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  80. val numClasses: Int

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

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

    Definition Classes
    FMClassificationModel → ClassificationModel
    Annotations
    @Since( "3.0.0" )
  81. 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
    FMClassificationModel → PredictionModel
    Annotations
    @Since( "3.0.0" )
  82. 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.

  83. var parent: Estimator[FMClassificationModel]

    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.

  84. 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.

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Definition Classes
    ClassificationModel → PredictionModel
  85. 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

    Definition Classes
    ProbabilisticClassificationModel
    Annotations
    @Since( "3.0.0" )
  86. 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.

    Definition Classes
    FMClassificationModel → ClassificationModel
    Annotations
    @Since( "3.0.0" )
  87. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  88. 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
    ProbabilisticClassificationModel
  89. 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
  90. 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
    ProbabilisticClassificationModel → ClassificationModel
  91. def raw2probability(rawPrediction: Vector): Vector

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  92. 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
    FMClassificationModel → ProbabilisticClassificationModel
  93. 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
  94. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  95. 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( ... )
  96. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  97. final def set(paramPair: ParamPair[_]): FMClassificationModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): FMClassificationModel.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
  99. final def set[T](param: Param[T], value: T): FMClassificationModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  100. final def setDefault(paramPairs: ParamPair[_]*): FMClassificationModel.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
  101. final def setDefault[T](param: Param[T], value: T): FMClassificationModel.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
  102. def setFeaturesCol(value: String): FMClassificationModel

    Definition Classes
    PredictionModel
  103. def setParent(parent: Estimator[FMClassificationModel]): FMClassificationModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  104. def setPredictionCol(value: String): FMClassificationModel

    Definition Classes
    PredictionModel
  105. def setProbabilityCol(value: String): FMClassificationModel

    Definition Classes
    ProbabilisticClassificationModel
  106. def setRawPredictionCol(value: String): FMClassificationModel

    Definition Classes
    ClassificationModel
  107. def setThresholds(value: Array[Double]): FMClassificationModel

    Definition Classes
    ProbabilisticClassificationModel
  108. final val solver: Param[String]

    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "gd", "adamW". Default: "adamW"

    Definition Classes
    FactorizationMachinesParams → HasSolver
    Annotations
    @Since( "3.0.0" )
  109. val stepSize: DoubleParam

    Param for Step size to be used for each iteration of optimization (> 0).

    Param for Step size to be used for each iteration of optimization (> 0).

    Definition Classes
    HasStepSize
  110. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  111. 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
  112. def toString(): String
    Definition Classes
    FMClassificationModel → Identifiable → AnyRef → Any
  113. 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
  114. 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
  115. 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" )
  116. 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()
  117. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModel → PredictionModel
  118. 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
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
  119. 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()
  120. 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
    FMClassificationModel → Identifiable
    Annotations
    @Since( "3.0.0" )
  121. 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
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  122. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  123. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  124. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  125. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    FMClassificationModel → MLWritable
    Annotations
    @Since( "3.0.0" )

Inherited from MLWritable

Inherited from FMClassifierParams

Inherited from FactorizationMachinesParams

Inherited from HasRegParam

Inherited from HasFitIntercept

Inherited from HasSeed

Inherited from HasSolver

Inherited from HasTol

Inherited from HasStepSize

Inherited from HasMaxIter

Inherited from ProbabilisticClassificationModel[Vector, FMClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, FMClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, FMClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[FMClassificationModel]

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