LogisticRegressionModel

Instance Constructors

new LogisticRegressionModel(weights: Vector, intercept: Double)

Constructs a LogisticRegressionModel with weights and intercept for binary classification.
Constructs a LogisticRegressionModel with weights and intercept for binary classification.

Annotations
@Since( "1.0.0" )
new LogisticRegressionModel(weights: Vector, intercept: Double, numFeatures: Int, numClasses: Int)

weights
Weights computed for every feature.
intercept
Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.)
numFeatures
the dimension of the features.
numClasses
the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

Annotations
@Since( "1.3.0" )

Value Members

final def !=(arg0: AnyRef): Boolean

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

Definition Classes
Any
final def ##(): Int

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

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

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clearThreshold(): LogisticRegressionModel.this.type

:: Experimental :: Clears the threshold so that predict will output raw prediction scores.
:: Experimental :: Clears the threshold so that predict will output raw prediction scores. It is only used for binary classification.

Annotations
@Since( "1.0.0" ) @Experimental()
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
LogisticRegressionModel → Saveable
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getThreshold: Option[Double]

:: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
:: Experimental :: Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. It is only used for binary classification.

Annotations
@Since( "1.3.0" ) @Experimental()
def hashCode(): Int

Definition Classes
AnyRef → Any
val intercept: Double

Intercept computed for this model.
Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.)

Definition Classes
LogisticRegressionModel → GeneralizedLinearModel
Annotations
@Since( "1.0.0" )
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
val numClasses: Int

the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression.
the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

Annotations
@Since( "1.3.0" )
val numFeatures: Int

the dimension of the features.
the dimension of the features.

Annotations
@Since( "1.3.0" )
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
ClassificationModel
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
LogisticRegressionModel → 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
LogisticRegressionModel → Saveable
Annotations
@Since( "1.3.0" )
def setThreshold(threshold: Double): LogisticRegressionModel.this.type

:: Experimental :: Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression.
:: Experimental :: Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. The default value is 0.5. It is only used for binary classification.

Annotations
@Since( "1.0.0" ) @Experimental()
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
LogisticRegressionModel → 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
LogisticRegressionModel → GeneralizedLinearModel
Annotations
@Since( "1.0.0" )

class LogisticRegressionModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable

Instance Constructors

new LogisticRegressionModel(weights: Vector, intercept: Double)

new LogisticRegressionModel(weights: Vector, intercept: Double, numFeatures: Int, numClasses: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clearThreshold(): LogisticRegressionModel.this.type

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 getThreshold: Option[Double]

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

val numClasses: Int

val numFeatures: Int

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

def setThreshold(threshold: Double): LogisticRegressionModel.this.type

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 ClassificationModel

Inherited from GeneralizedLinearModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped