RegressionModel

abstract def copy(extra: ParamMap): M

Creates a copy of this instance with the same UID and some extra params.

abstract def predict(features: FeaturesType): Double

Predict label for the given features.

abstract val uid: String

An immutable unique ID for the object and its derivatives.

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

An alias for getOrDefault().

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

Clears the user-supplied value for the input param.

def clone(): AnyRef

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.

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

Default implementation of copy with extra params.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

Explains a param.

def explainParams(): String

Explains all params of this instance.

final def extractParamMap(): ParamMap

extractParamMap with no extra values.

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.

final val featuresCol: Param[String]

Param for features column name.

def featuresDataType: DataType

Returns the SQL DataType corresponding to the FeaturesType type parameter.

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

Optionally returns the user-supplied value of a param.

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

Gets the default value of a parameter.

final def getFeaturesCol: String

final def getLabelCol: String

final def getOrDefault[T](param: Param[T]): T

Gets the value of a param in the embedded param map or its default value.

def getParam(paramName: String): Param[Any]

Gets a param by its name.

final def getPredictionCol: String

final def hasDefault[T](param: Param[T]): Boolean

Tests whether the input param has a default value set.

def hasParam(paramName: String): Boolean

Tests whether this instance contains a param with a given name.

def hasParent: Boolean

Indicates whether this Model has a corresponding parent.

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isDefined(param: Param[_]): Boolean

Checks whether a param is explicitly set or has a default value.

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

Checks whether a param is explicitly set.

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

Param for label column name.

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numFeatures: Int

Returns the number of features the model was trained on.

lazy val params: Array[Param[_]]

Returns all params sorted by their names.

var parent: Estimator[M]

The parent estimator that produced this model.

final val predictionCol: Param[String]

Param for prediction column name.

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

Sets a parameter in the embedded param map.

final def set(param: String, value: Any): RegressionModel.this.type

Sets a parameter (by name) in the embedded param map.

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

Sets a parameter in the embedded param map.

final def setDefault(paramPairs: ParamPair[_]*): RegressionModel.this.type

Sets default values for a list of params.

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

Sets a default value for a param.

def setFeaturesCol(value: String): M

def setParent(parent: Estimator[M]): M

Sets the parent of this model (Java API).

def setPredictionCol(value: String): M

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def transform(dataset: Dataset[_]): DataFrame

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

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

Transforms the dataset with provided parameter map as additional parameters.

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

Transforms the dataset with optional parameters

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

def transformSchema(schema: StructType): StructType

:: DeveloperApi ::

def transformSchema(schema: StructType, logging: Boolean): StructType

:: DeveloperApi ::

def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Validates and transforms the input schema with the provided param map.

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit