LinearRegressionModel

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[_]): LinearRegressionModel.this.type

Clears the user-supplied value for the input param.

def clone(): AnyRef

val coefficients: Vector

def copy(extra: ParamMap): LinearRegressionModel

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

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 val elasticNetParam: DoubleParam

Param for the ElasticNet mixing parameter, in range [0, 1].

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evaluate(dataset: Dataset[_]): LinearRegressionSummary

Evaluates the model on a test dataset.

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 val fitIntercept: BooleanParam

Param for whether to fit an intercept term.

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 getElasticNetParam: Double

final def getFeaturesCol: String

final def getFitIntercept: Boolean

final def getLabelCol: String

final def getMaxIter: Int

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 getRegParam: Double

final def getSolver: String

final def getStandardization: Boolean

final def getTol: Double

final def getWeightCol: 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 hasSummary: Boolean

Indicates whether a training summary exists for this model instance.

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

val intercept: Double

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 val maxIter: IntParam

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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val 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[LinearRegressionModel]

The parent estimator that produced this model.

def predict(features: Vector): Double

Predict label for the given features.

final val predictionCol: Param[String]

Param for prediction column name.

final val regParam: DoubleParam

Param for regularization parameter (>= 0).

def save(path: String): Unit

Saves this ML instance to the input path, a shortcut of write.save(path).

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

Sets a parameter in the embedded param map.

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

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

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

Sets a parameter in the embedded param map.

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

Sets default values for a list of params.

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

Sets a default value for a param.

def setFeaturesCol(value: String): LinearRegressionModel

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

Sets the parent of this model (Java API).

def setPredictionCol(value: String): LinearRegressionModel

final val solver: Param[String]

Param for the solver algorithm for optimization.

final val standardization: BooleanParam

Param for whether to standardize the training features before fitting the model.

def summary: LinearRegressionTrainingSummary

Gets summary (e.g.

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

def toString(): String

final val tol: DoubleParam

Param for the convergence tolerance for iterative algorithms.

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 ::

val uid: String

An immutable unique ID for the object and its derivatives.

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

final val weightCol: Param[String]

Param for weight column name.

def write: MLWriter

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

def validateParams(): Unit

Validates parameter values stored internally.