RidgeRegressionModel

Instance Constructors

new RidgeRegressionModel(weights: Vector, intercept: Double)

weights
Weights computed for every feature.
intercept
Intercept computed for this model.

Annotations
@Since( "1.1.0" )

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

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

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatVersion: String

Current version of model save/load format.
Current version of model save/load format.

Attributes
protected
Definition Classes
RidgeRegressionModel → Saveable
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
val intercept: Double

Intercept computed for this model.
Intercept computed for this model.

Definition Classes
RidgeRegressionModel → GeneralizedLinearModel
Annotations
@Since( "0.8.0" )
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
testData
JavaRDD representing data points to be predicted
returns
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

Definition Classes
RegressionModel
Annotations
@Since( "1.0.0" )
def predict(testData: Vector): Double

Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
testData
array representing a single data point
returns
Double prediction from the trained model

Definition Classes
GeneralizedLinearModel
Annotations
@Since( "1.0.0" )
def predict(testData: RDD[Vector]): RDD[Double]

Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
testData
RDD representing data points to be predicted
returns
RDD[Double] where each entry contains the corresponding prediction

Definition Classes
GeneralizedLinearModel
Annotations
@Since( "1.0.0" )
def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double

Predict the result given a data point and the weights learned.
Predict the result given a data point and the weights learned.
dataMatrix
Row vector containing the features for this data point
weightMatrix
Column vector containing the weights of the model
intercept
Intercept of the model.

Attributes
protected
Definition Classes
RidgeRegressionModel → GeneralizedLinearModel
def save(sc: SparkContext, path: String): Unit

Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using Loader.load.
sc
Spark context used to save model data.
path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

Definition Classes
RidgeRegressionModel → Saveable
Annotations
@Since( "1.3.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toPMML(): String

:: Experimental :: Export the model to a String in PMML format
:: Experimental :: Export the model to a String in PMML format

Definition Classes
PMMLExportable
Annotations
@Experimental() @Since( "1.4.0" )
def toPMML(outputStream: OutputStream): Unit

:: Experimental :: Export the model to the OutputStream in PMML format
:: Experimental :: Export the model to the OutputStream in PMML format

Definition Classes
PMMLExportable
Annotations
@Experimental() @Since( "1.4.0" )
def toPMML(sc: SparkContext, path: String): Unit

:: Experimental :: Export the model to a directory on a distributed file system in PMML format
:: Experimental :: Export the model to a directory on a distributed file system in PMML format

Definition Classes
PMMLExportable
Annotations
@Experimental() @Since( "1.4.0" )
def toPMML(localPath: String): Unit

:: Experimental :: Export the model to a local file in PMML format
:: Experimental :: Export the model to a local file in PMML format

Definition Classes
PMMLExportable
Annotations
@Experimental() @Since( "1.4.0" )
def toString(): String

Print a summary of the model.
Print a summary of the model.

Definition Classes
GeneralizedLinearModel → AnyRef → Any
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )
val weights: Vector

Weights computed for every feature.
Weights computed for every feature.

Definition Classes
RidgeRegressionModel → GeneralizedLinearModel
Annotations
@Since( "1.0.0" )

Related Docs: object RidgeRegressionModel | package regression

class RidgeRegressionModel extends GeneralizedLinearModel with RegressionModel with Serializable with Saveable with PMMLExportable

Instance Constructors

new RidgeRegressionModel(weights: Vector, intercept: Double)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatVersion: String

final def getClass(): Class[_]

def hashCode(): Int

val intercept: Double

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

def predict(testData: Vector): Double

def predict(testData: RDD[Vector]): RDD[Double]

def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double

def save(sc: SparkContext, path: String): Unit

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

def toPMML(): String

def toPMML(outputStream: OutputStream): Unit

def toPMML(sc: SparkContext, path: String): Unit

def toPMML(localPath: String): Unit

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val weights: Vector

Inherited from PMMLExportable

Inherited from Saveable

Inherited from RegressionModel

Inherited from GeneralizedLinearModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped