LogisticRegression

Instance Constructors

new LogisticRegression()

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 explainParams(): String

Returns the documentation of all params.
Returns the documentation of all params.

Definition Classes
Params
val featuresCol: Param[String]

param for features column name
param for features column name

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel

Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
dataset
input dataset
paramMap
parameter map
returns
fitted model

Definition Classes
LogisticRegression → Estimator
def fit(dataset: JavaSchemaRDD, paramMaps: Array[ParamMap]): List[LogisticRegressionModel]

Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters.
dataset
input dataset
paramMaps
an array of parameter maps
returns
fitted models, matching the input parameter maps

Definition Classes
Estimator
def fit(dataset: JavaSchemaRDD, paramMap: ParamMap): LogisticRegressionModel

Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
dataset
input dataset
paramMap
parameter map
returns
fitted model

Definition Classes
Estimator
def fit(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
dataset
input dataset
paramPairs
optional list of param pairs (overwrite embedded params)
returns
fitted model

Definition Classes
Estimator
Annotations
@varargs()
def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[LogisticRegressionModel]

Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could overwrite this to optimize multi-model training.
dataset
input dataset
paramMaps
an array of parameter maps
returns
fitted models, matching the input parameter maps

Definition Classes
Estimator
def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
dataset
input dataset
paramPairs
optional list of param pairs (overwrite embedded params)
returns
fitted model

Definition Classes
Estimator
Annotations
@varargs()
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getFeaturesCol: String

Definition Classes
HasFeaturesCol
def getLabelCol: String

Definition Classes
HasLabelCol
def getMaxIter: Int

Definition Classes
HasMaxIter
def getPredictionCol: String

Definition Classes
HasPredictionCol
def getRegParam: Double

Definition Classes
HasRegParam
def getScoreCol: String

Definition Classes
HasScoreCol
def getThreshold: Double

Definition Classes
HasThreshold
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def isSet(param: Param[_]): Boolean

Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
val labelCol: Param[String]

param for label column name
param for label column name

Definition Classes
HasLabelCol
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): 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(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): 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(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
val maxIter: IntParam

param for max number of iterations
param for max number of iterations

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val paramMap: ParamMap

Internal param map.
Internal param map.

Attributes
protected
Definition Classes
Params
def params: Array[Param[_]]

Returns all params.
Returns all params.

Definition Classes
Params
val predictionCol: Param[String]

param for prediction column name
param for prediction column name

Definition Classes
HasPredictionCol
val regParam: DoubleParam

param for regularization parameter
param for regularization parameter

Definition Classes
HasRegParam
val scoreCol: Param[String]

param for score column name
param for score column name

Definition Classes
HasScoreCol
def setFeaturesCol(value: String): LogisticRegression.this.type
def setLabelCol(value: String): LogisticRegression.this.type
def setMaxIter(value: Int): LogisticRegression.this.type
def setPredictionCol(value: String): LogisticRegression.this.type
def setRegParam(value: Double): LogisticRegression.this.type
def setScoreCol(value: String): LogisticRegression.this.type
def setThreshold(value: Double): LogisticRegression.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val threshold: DoubleParam

param for threshold in (binary) prediction
param for threshold in (binary) prediction

Definition Classes
HasThreshold
def toString(): String

Definition Classes
AnyRef → Any
def transformSchema(schema: StructType, paramMap: ParamMap, logging: Boolean): StructType

Derives the output schema from the input schema and parameters, optionally with logging.
Derives the output schema from the input schema and parameters, optionally with logging.

Attributes
protected
Definition Classes
PipelineStage
def validate(): Unit

Validates parameter values stored internally.
Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

Definition Classes
Params
def validate(paramMap: ParamMap): Unit

Validates parameter values stored internally plus the input parameter map.
Validates parameter values stored internally plus the input parameter map. Raises an exception if any parameter is invalid.

Definition Classes
Params
def validateAndTransformSchema(schema: StructType, paramMap: ParamMap, fitting: Boolean): StructType

Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
schema
input schema
paramMap
additional parameters
fitting
whether this is in fitting
returns
output schema

Attributes
protected
Definition Classes
LogisticRegressionParams
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( ... )

class LogisticRegression extends Estimator[LogisticRegressionModel] with LogisticRegressionParams

Instance Constructors

new LogisticRegression()

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 explainParams(): String

val featuresCol: Param[String]

def finalize(): Unit

def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel

def fit(dataset: JavaSchemaRDD, paramMaps: Array[ParamMap]): List[LogisticRegressionModel]

def fit(dataset: JavaSchemaRDD, paramMap: ParamMap): LogisticRegressionModel

def fit(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[LogisticRegressionModel]

def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): LogisticRegressionModel

final def getClass(): Class[_]

def getFeaturesCol: String

def getLabelCol: String

def getMaxIter: Int

def getPredictionCol: String

def getRegParam: Double

def getScoreCol: String

def getThreshold: Double

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

val labelCol: Param[String]

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

val maxIter: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val paramMap: ParamMap

def params: Array[Param[_]]

val predictionCol: Param[String]

val regParam: DoubleParam

val scoreCol: Param[String]

def setFeaturesCol(value: String): LogisticRegression.this.type

def setLabelCol(value: String): LogisticRegression.this.type

def setMaxIter(value: Int): LogisticRegression.this.type

def setPredictionCol(value: String): LogisticRegression.this.type

def setRegParam(value: Double): LogisticRegression.this.type

def setScoreCol(value: String): LogisticRegression.this.type

def setThreshold(value: Double): LogisticRegression.this.type

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

val threshold: DoubleParam

def toString(): String

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

def validate(): Unit

def validate(paramMap: ParamMap): Unit

def validateAndTransformSchema(schema: StructType, paramMap: ParamMap, fitting: Boolean): StructType

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from LogisticRegressionParams

Inherited from HasPredictionCol

Inherited from HasScoreCol

Inherited from HasFeaturesCol

Inherited from HasThreshold

Inherited from HasLabelCol

Inherited from HasMaxIter

Inherited from HasRegParam