LogisticRegressionModel

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
def addOutputColumn(schema: StructType, colName: String, dataType: DataType): StructType

Attributes
protected
Definition Classes
Params
final def asInstanceOf[T0]: T0

Definition Classes
Any
def checkInputColumn(schema: StructType, colName: String, dataType: DataType): Unit

Check whether the given schema contains an input column.
Check whether the given schema contains an input column.
colName
Parameter name for the input column.
dataType
SQL DataType of the input column.

Attributes
protected
Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(): LogisticRegressionModel

Create a copy of the model.
Create a copy of the model. The copy is shallow, except for the embedded paramMap, which gets a deep copy.

Attributes
protected
Definition Classes
LogisticRegressionModel → PredictionModel
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 featuresDataType: DataType

:: DeveloperApi ::
:: DeveloperApi ::
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
Annotations
@DeveloperApi()
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val fittingParamMap: ParamMap

Fitting parameters, such that parent.
Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model.

Definition Classes
LogisticRegressionModel → Model
def get[T](param: Param[T]): T

Gets the value of a parameter in the embedded param map.
Gets the value of a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
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 getProbabilityCol: String

Definition Classes
HasProbabilityCol
def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
def getRegParam: Double

Definition Classes
HasRegParam
def getThreshold: Double

Definition Classes
HasThreshold
def hashCode(): Int

Definition Classes
AnyRef → Any
val intercept: Double
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 numClasses: Int

Number of classes (values which the label can take).
Number of classes (values which the label can take).

Definition Classes
LogisticRegressionModel → ClassificationModel
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 parent: LogisticRegression

The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
LogisticRegressionModel → Model
def predict(features: Vector): Double

Predict label for the given feature vector.
Predict label for the given feature vector. The behavior of this can be adjusted using threshold.

Attributes
protected
Definition Classes
LogisticRegressionModel → ClassificationModel → PredictionModel
def predictProbabilities(features: Vector): Vector

:: DeveloperApi ::
:: DeveloperApi ::
Predict the probability of each class given the features. These predictions are also called class conditional probabilities.
WARNING: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
This internal method is used to implement transform() and output probabilityCol.

Attributes
protected
Definition Classes
LogisticRegressionModel → ProbabilisticClassificationModel
def predictRaw(features: Vector): Vector

:: DeveloperApi ::
:: DeveloperApi ::
Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.
returns
vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

Attributes
protected
Definition Classes
LogisticRegressionModel → ClassificationModel
val predictionCol: Param[String]

param for prediction column name
param for prediction column name

Definition Classes
HasPredictionCol
val probabilityCol: Param[String]

param for predicted class conditional probabilities column name
param for predicted class conditional probabilities column name

Definition Classes
HasProbabilityCol
val rawPredictionCol: Param[String]

param for raw prediction column name
param for raw prediction column name

Definition Classes
HasRawPredictionCol
val regParam: DoubleParam

param for regularization parameter
param for regularization parameter

Definition Classes
HasRegParam
def set[T](param: Param[T], value: T): LogisticRegressionModel.this.type

Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
def setFeaturesCol(value: String): LogisticRegressionModel

Definition Classes
PredictionModel
def setPredictionCol(value: String): LogisticRegressionModel

Definition Classes
PredictionModel
def setProbabilityCol(value: String): LogisticRegressionModel

Definition Classes
ProbabilisticClassificationModel
def setRawPredictionCol(value: String): LogisticRegressionModel

Definition Classes
ClassificationModel
def setThreshold(value: Double): LogisticRegressionModel.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 transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
LogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame

Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
paramPairs
optional list of param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@varargs()
def transformSchema(schema: StructType, paramMap: ParamMap): StructType

:: DeveloperAPI ::
:: DeveloperAPI ::
Derives the output schema from the input schema and parameters. The schema describes the columns and types of the data.
schema
Input schema to this stage
paramMap
Parameters passed to this stage
returns
Output schema from this stage

Definition Classes
PredictionModel → PipelineStage
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, featuresDataType: DataType): 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
featuresDataType
SQL DataType for FeaturesType. E.g., org.apache.spark.mllib.linalg.VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
ProbabilisticClassifierParams → ClassifierParams → PredictorParams
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

Inherited from LogisticRegressionParams

Inherited from HasThreshold

Inherited from HasMaxIter

Inherited from HasRegParam

Inherited from ProbabilisticClassificationModel[Vector, LogisticRegressionModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, LogisticRegressionModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, LogisticRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[LogisticRegressionModel]

Inherited from Transformer

Inherited from Params

Inherited from Identifiable

Inherited from PipelineStage

Inherited from Logging

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams

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

def addOutputColumn(schema: StructType, colName: String, dataType: DataType): StructType

final def asInstanceOf[T0]: T0

def checkInputColumn(schema: StructType, colName: String, dataType: DataType): Unit

def clone(): AnyRef

def copy(): LogisticRegressionModel

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParams(): String

val featuresCol: Param[String]

def featuresDataType: DataType

def finalize(): Unit

val fittingParamMap: ParamMap

def get[T](param: Param[T]): T

final def getClass(): Class[_]

def getFeaturesCol: String

def getLabelCol: String

def getMaxIter: Int

def getPredictionCol: String

def getProbabilityCol: String

def getRawPredictionCol: String

def getRegParam: Double

def getThreshold: Double

def hashCode(): Int

val intercept: Double

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 numClasses: Int

val paramMap: ParamMap

def params: Array[Param[_]]

val parent: LogisticRegression

def predict(features: Vector): Double

def predictProbabilities(features: Vector): Vector

def predictRaw(features: Vector): Vector

val predictionCol: Param[String]

val probabilityCol: Param[String]

val rawPredictionCol: Param[String]

val regParam: DoubleParam

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

def setFeaturesCol(value: String): LogisticRegressionModel

def setPredictionCol(value: String): LogisticRegressionModel

def setProbabilityCol(value: String): LogisticRegressionModel

def setRawPredictionCol(value: String): LogisticRegressionModel

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

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

val threshold: DoubleParam

def toString(): String

def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame

def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame

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

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, featuresDataType: DataType): StructType

final def wait(): Unit