LogisticRegressionWithLBFGS

class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default.

Earlier implementations of LogisticRegressionWithLBFGS applies a regularization penalty to all elements including the intercept. If this is called with one of standard updaters (L1Updater, or SquaredL2Updater) this is translated into a call to ml.LogisticRegression, otherwise this will use the existing mllib GeneralizedLinearAlgorithm trainer, resulting in a regularization penalty to the intercept.

Annotations: @Since("1.1.0")
Source: LogisticRegression.scala
Note: Labels used in Logistic Regression should be {0, 1, ..., k - 1} for k classes multi-label classification problem.

Linear Supertypes

GeneralizedLinearAlgorithm[LogisticRegressionModel], Serializable, Logging, AnyRef, Any

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LogisticRegressionWithLBFGS
GeneralizedLinearAlgorithm
Serializable
Logging
AnyRef
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Instance Constructors

new LogisticRegressionWithLBFGS()

Type Members

implicit class LogStringContext extends AnyRef
Definition Classes
Logging

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
var addIntercept: Boolean
Whether to add intercept (default: false).
Whether to add intercept (default: false).
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
final def asInstanceOf[T0]: T0
Definition Classes
Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def createModel(weights: Vector, intercept: Double): LogisticRegressionModel
Create a model given the weights and intercept
Create a model given the weights and intercept
Attributes
protected
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
def generateInitialWeights(input: RDD[LabeledPoint]): Vector
Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def getNumFeatures: Int
The dimension of training features.
The dimension of training features.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
def isAddIntercept: Boolean
Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
def isTraceEnabled(): Boolean
Attributes
protected
Definition Classes
Logging
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logName: String
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
var numFeatures: Int
The dimension of training features.
The dimension of training features.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
var numOfLinearPredictor: Int
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.
As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.
Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
val optimizer: LBFGS
The optimizer to solve the problem.
The optimizer to solve the problem.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
Annotations
@Since("1.1.0")
def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. Uses user provided weights.
In the ml LogisticRegression implementation, the number of corrections used in the LBFGS update can not be configured. So optimizer.setNumCorrections() will have no effect if we fall into that route.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
def run(input: RDD[LabeledPoint]): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. If using ml implementation, uses ml code to generate initial weights.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
def setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
def setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so k will be set to 2.
Annotations
@Since("1.3.0")
def setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
AnyRef → Any
var validateData: Boolean
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
val validators: List[(RDD[LabeledPoint]) => Boolean]
Attributes
protected
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging

Inherited from GeneralizedLinearAlgorithm[LogisticRegressionModel]

Value Members

var addIntercept: Boolean
Whether to add intercept (default: false).
Whether to add intercept (default: false).
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
def generateInitialWeights(input: RDD[LabeledPoint]): Vector
Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
def getNumFeatures: Int
The dimension of training features.
The dimension of training features.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
def isAddIntercept: Boolean
Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
var numFeatures: Int
The dimension of training features.
The dimension of training features.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
var numOfLinearPredictor: Int
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.
As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.
Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
def setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
def setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
var validateData: Boolean
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm

Inherited from Logging

Value Members

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
def isTraceEnabled(): Boolean
Attributes
protected
Definition Classes
Logging
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logName: String
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging

Inherited from AnyRef

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
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
AnyRef → Any
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Inherited from Any

Value Members

final def asInstanceOf[T0]: T0
Definition Classes
Any
final def isInstanceOf[T0]: Boolean
Definition Classes
Any

Ungrouped

implicit class LogStringContext extends AnyRef
Definition Classes
Logging

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
var addIntercept: Boolean
Whether to add intercept (default: false).
Whether to add intercept (default: false).
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
final def asInstanceOf[T0]: T0
Definition Classes
Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def createModel(weights: Vector, intercept: Double): LogisticRegressionModel
Create a model given the weights and intercept
Create a model given the weights and intercept
Attributes
protected
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
def generateInitialWeights(input: RDD[LabeledPoint]): Vector
Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def getNumFeatures: Int
The dimension of training features.
The dimension of training features.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
def isAddIntercept: Boolean
Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("1.4.0")
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
def isTraceEnabled(): Boolean
Attributes
protected
Definition Classes
Logging
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logError(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logName: String
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
var numFeatures: Int
The dimension of training features.
The dimension of training features.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
var numOfLinearPredictor: Int
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept.
In GeneralizedLinearModel, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.
As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger weights vector which can hold both weights and intercepts. If the intercepts are added, the dimension of weights will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension of weights will be (numOfLinearPredictor) * numFeatures.
Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
val optimizer: LBFGS
The optimizer to solve the problem.
The optimizer to solve the problem.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
Annotations
@Since("1.1.0")
def run(input: RDD[LabeledPoint], initialWeights: Vector): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. Uses user provided weights.
In the ml LogisticRegression implementation, the number of corrections used in the LBFGS update can not be configured. So optimizer.setNumCorrections() will have no effect if we fall into that route.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
def run(input: RDD[LabeledPoint]): LogisticRegressionModel
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
Run Logistic Regression with the configured parameters on an input RDD of LabeledPoint entries.
If a known updater is used calls the ml implementation, to avoid applying a regularization penalty to the intercept, otherwise defaults to the mllib implementation. If more than two classes or feature scaling is disabled, always uses mllib implementation. If using ml implementation, uses ml code to generate initial weights.
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
def setIntercept(addIntercept: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
def setNumClasses(numClasses: Int): LogisticRegressionWithLBFGS.this.type
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.
Set the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so k will be set to 2.
Annotations
@Since("1.3.0")
def setValidateData(validateData: Boolean): LogisticRegressionWithLBFGS.this.type
Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.
Definition Classes
GeneralizedLinearAlgorithm
Annotations
@Since("0.8.0")
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
AnyRef → Any
var validateData: Boolean
Attributes
protected
Definition Classes
GeneralizedLinearAlgorithm
val validators: List[(RDD[LabeledPoint]) => Boolean]
Attributes
protected
Definition Classes
LogisticRegressionWithLBFGS → GeneralizedLinearAlgorithm
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

LogisticRegressionWithLBFGS

class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Instance Constructors

Type Members

Value Members

Deprecated Value Members

Inherited from GeneralizedLinearAlgorithm[LogisticRegressionModel]

Value Members

Inherited from Logging

Value Members

Inherited from AnyRef

Value Members

Inherited from Any

Value Members

Ungrouped

Packages

LogisticRegressionWithLBFGS

class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

Instance Constructors

Type Members

Value Members

Deprecated Value Members

Inherited from GeneralizedLinearAlgorithm[LogisticRegressionModel]

Value Members

Inherited from Logging

Value Members

Inherited from AnyRef

Value Members

Inherited from Any

Value Members

Ungrouped

LogisticRegressionWithLBFGS