org.apache.spark.ml.classification

Class LogisticRegressionModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<M>

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>

org.apache.spark.ml.classification.LogisticRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public class LogisticRegressionModel
extends ClassificationModel<FeaturesType,M>

:: Experimental :: Model produced by LogisticRegression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
LogisticRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
double	intercept()
int	numClasses() Number of classes (values which the label can take).
M	setProbabilityCol(String value)
LogisticRegressionModel	setThreshold(double value)
DataFrame	transform(DataFrame dataset) 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.
String	uid()
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
Vector	weights()

Methods inherited from class org.apache.spark.ml.classification.ClassificationModel

setRawPredictionCol

Methods inherited from class org.apache.spark.ml.PredictionModel

setFeaturesCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

uid

public String uid()

weights

public Vector weights()

intercept

public double intercept()

setThreshold

public LogisticRegressionModel setThreshold(double value)

numClasses

public int numClasses()

Description copied from class: ClassificationModel

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

Specified by:

numClasses in class ClassificationModel<Vector,LogisticRegressionModel>

copy

public LogisticRegressionModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. The default implementation tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance. Subclasses should override this method if the default approach is not sufficient.

Specified by:

copy in interface Params

Overrides:

copy in class Model<LogisticRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

setProbabilityCol

public M setProbabilityCol(String value)

transform

public DataFrame transform(DataFrame dataset)

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.

Overrides:

transform in class ClassificationModel<FeaturesType,M extends org.apache.spark.ml.classification.ProbabilisticClassificationModel<FeaturesType,M>>

Parameters:

dataset - input dataset

Returns:

transformed dataset

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema