org.apache.spark.ml.classification

Class LogisticRegression

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.LogisticRegression

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public class LogisticRegression
extends Classifier<FeaturesType,E,M>
implements Logging

:: Experimental :: Logistic regression. Currently, this class only supports binary classification.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegression()
LogisticRegression(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
LogisticRegression	setElasticNetParam(double value) Set the ElasticNet mixing parameter.
LogisticRegression	setFitIntercept(boolean value) Whether to fit an intercept term.
LogisticRegression	setMaxIter(int value) Set the maximum number of iterations.
E	setProbabilityCol(String value)
LogisticRegression	setRegParam(double value) Set the regularization parameter.
LogisticRegression	setThreshold(double value)
LogisticRegression	setTol(double value) Set the convergence tolerance of iterations.
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.

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

setRawPredictionCol

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

copy, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

fit, fit, fit, fit

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.Logging

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

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

Constructor Detail

LogisticRegression

public LogisticRegression(String uid)

LogisticRegression

public LogisticRegression()

Method Detail

uid

public String uid()

setRegParam

public LogisticRegression setRegParam(double value)

Set the regularization parameter. Default is 0.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setElasticNetParam

public LogisticRegression setElasticNetParam(double value)

Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public LogisticRegression setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public LogisticRegression setTol(double value)

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setFitIntercept

public LogisticRegression setFitIntercept(boolean value)

Whether to fit an intercept term. Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setThreshold

public LogisticRegression setThreshold(double value)

validateAndTransformSchema

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

setProbabilityCol

public E setProbabilityCol(String value)

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