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.impl.estimator.PredictionModel<FeaturesType,M>

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

org.apache.spark.ml.classification.ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

org.apache.spark.ml.classification.LogisticRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, ClassifierParams, LogisticRegressionParams, ProbabilisticClassifierParams, Identifiable, PredictorParams, HasFeaturesCol, HasLabelCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasRegParam, HasThreshold, Params

public class LogisticRegressionModel
extends ProbabilisticClassificationModel<Vector,LogisticRegressionModel>
implements LogisticRegressionParams

:: AlphaComponent ::

Model produced by LogisticRegression.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionModel(LogisticRegression parent, ParamMap fittingParamMap, Vector weights, double intercept)

Method Summary

Methods

Modifier and Type	Method and Description
ParamMap	fittingParamMap() Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model.
double	intercept()
int	numClasses() Number of classes (values which the label can take).
LogisticRegression	parent() The parent estimator that produced this model.
LogisticRegressionModel	setThreshold(double value)
DataFrame	transform(DataFrame dataset, ParamMap paramMap) 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.
Vector	weights()

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

setProbabilityCol

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

setRawPredictionCol, transformColumnsImpl

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

setFeaturesCol, setPredictionCol, transformSchema

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

transform, transform

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.classification.ProbabilisticClassifierParams

validateAndTransformSchema

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

getLabelCol, labelCol

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

featuresCol, getFeaturesCol

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

getPredictionCol, predictionCol

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

addOutputColumn, checkInputColumn, explainParams, get, getParam, isSet, paramMap, params, set, set, validate, validate

Methods inherited from interface org.apache.spark.ml.Identifiable

uid

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

getRawPredictionCol, rawPredictionCol

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

getProbabilityCol, probabilityCol

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

getRegParam, regParam

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

getMaxIter, maxIter

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

getThreshold, threshold

Methods inherited from interface org.apache.spark.Logging

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

Constructor Detail

LogisticRegressionModel

public LogisticRegressionModel(LogisticRegression parent,
                       ParamMap fittingParamMap,
                       Vector weights,
                       double intercept)

Method Detail

parent

public LogisticRegression parent()

Description copied from class: Model

The parent estimator that produced this model.

Specified by:

parent in class Model<LogisticRegressionModel>

fittingParamMap

public ParamMap fittingParamMap()

Description copied from class: Model

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

Specified by:

fittingParamMap in class Model<LogisticRegressionModel>

weights

public Vector weights()

intercept

public double intercept()

setThreshold

public LogisticRegressionModel setThreshold(double value)

transform

public DataFrame transform(DataFrame dataset,
                  ParamMap paramMap)

Description copied from class: ProbabilisticClassificationModel

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 ProbabilisticClassificationModel<Vector,LogisticRegressionModel>

Parameters:

dataset - input dataset

paramMap - additional parameters, overwrite embedded params

Returns:

transformed dataset

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>