LogisticRegression

Companion object LogisticRegression

class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

Logistic regression. Supports:

Multinomial logistic (softmax) regression.
Binomial logistic regression.

This class supports fitting traditional logistic regression model by LBFGS/OWLQN and bound (box) constrained logistic regression model by LBFGSB.

Since 3.1.0, it supports stacking instances into blocks and using GEMV/GEMM for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.

Annotations: @Since( "1.2.0" )
Source: LogisticRegression.scala

Linear Supertypes

DefaultParamsWritable, MLWritable, LogisticRegressionParams, HasMaxBlockSizeInMB, HasAggregationDepth, HasThreshold, HasWeightCol, HasStandardization, HasTol, HasFitIntercept, HasMaxIter, HasElasticNetParam, HasRegParam, ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, LogisticRegression, LogisticRegressionModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, LogisticRegression, LogisticRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[LogisticRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

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Inherited

LogisticRegression
DefaultParamsWritable
MLWritable
LogisticRegressionParams
HasMaxBlockSizeInMB
HasAggregationDepth
HasThreshold
HasWeightCol
HasStandardization
HasTol
HasFitIntercept
HasMaxIter
HasElasticNetParam
HasRegParam
ProbabilisticClassifier
ProbabilisticClassifierParams
HasThresholds
HasProbabilityCol
Classifier
ClassifierParams
HasRawPredictionCol
Predictor
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Estimator
PipelineStage
Logging
Params
Serializable
Serializable
Identifiable
AnyRef
Any

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Visibility

Public
All

Instance Constructors

new LogisticRegression()

Annotations
@Since( "1.4.0" )
new LogisticRegression(uid: String)

Annotations
@Since( "1.2.0" )

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).

Definition Classes
HasAggregationDepth
final def asInstanceOf[T0]: T0

Definition Classes
Any
def checkThresholdConsistency(): Unit
If threshold and thresholds are both set, ensures they are consistent.
If threshold and thresholds are both set, ensures they are consistent.

Attributes
protected
Definition Classes
LogisticRegressionParams
Exceptions thrown
IllegalArgumentException if threshold and thresholds are not equivalent
final def clear(param: Param[_]): LogisticRegression.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): LogisticRegression
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Definition Classes
LogisticRegression → Predictor → Estimator → PipelineStage → Params
Annotations
@Since( "1.4.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final val elasticNetParam: DoubleParam
Param for the ElasticNet mixing parameter, in range [0, 1].
Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

Definition Classes
HasElasticNetParam
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
param
input param, must belong to this instance.
returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value

Definition Classes
Params
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

Attributes
protected
Definition Classes
ClassifierParams
def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
dataset
DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
Note
Throws SparkException if any label is a non-integer or is negative
def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
Predictor
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Definition Classes
Params
final val family: Param[String]
Param for the name of family which is a description of the label distribution to be used in the model.
Param for the name of family which is a description of the label distribution to be used in the model. Supported options:
- "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
- "binomial": Binary logistic regression with pivoting.
- "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".
Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): LogisticRegressionModel
Fits a model to the input data.
Fits a model to the input data.

Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Seq[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 override this to optimize multi-model training.
dataset
input dataset
paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted models, matching the input parameter maps

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], 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. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: 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
firstParamPair
the first param pair, overrides embedded params
otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.

Definition Classes
HasFitIntercept
final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.

Definition Classes
Params
final def getAggregationDepth: Int

Definition Classes
HasAggregationDepth
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.

Definition Classes
Params
final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam
def getFamily: String

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLowerBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getLowerBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
final def getMaxBlockSizeInMB: Double

Definition Classes
HasMaxBlockSizeInMB
final def getMaxIter: Int

Definition Classes
HasMaxIter
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int
Get the number of classes.
Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.
Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().
dataset
Dataset which contains a column labelCol
maxNumClasses
Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.
returns
number of classes

Attributes
protected
Definition Classes
Classifier
Exceptions thrown
IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses
final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Definition Classes
Params
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
final def getStandardization: Boolean

Definition Classes
HasStandardization
def getThreshold: Double
Get threshold for binary classification.
Get threshold for binary classification.
If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:
```
1 / (1 + thresholds(0) / thresholds(1))
```
. Otherwise, returns threshold if set, or its default value if unset.
1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold if set, or its default value if unset.
Definition Classes
LogisticRegression → LogisticRegressionParams → HasThreshold
Annotations
@Since( "1.5.0" )
Exceptions thrown
IllegalArgumentException if thresholds is set to an array of length other than 2.
def getThresholds: Array[Double]
Get thresholds for binary or multiclass classification.
Get thresholds for binary or multiclass classification.
If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

Definition Classes
LogisticRegression → LogisticRegressionParams → HasThresholds
Annotations
@Since( "1.5.0" )
final def getTol: Double

Definition Classes
HasTol
def getUpperBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getUpperBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
final def getWeightCol: String

Definition Classes
HasWeightCol
final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.

Definition Classes
Params
def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final 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
final 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 lowerBoundsOnCoefficients: Param[Matrix]
The lower bounds on coefficients if fitting under bound constrained optimization.
The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val lowerBoundsOnIntercepts: Param[Vector]
The lower bounds on intercepts if fitting under bound constrained optimization.
The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
final val maxBlockSizeInMB: DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.
Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

Definition Classes
HasMaxBlockSizeInMB
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

Definition Classes
Params
Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol
final val rawPredictionCol: Param[String]
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.

Definition Classes
HasRawPredictionCol
final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).

Definition Classes
HasRegParam
def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): LogisticRegression.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
def setAggregationDepth(value: Int): LogisticRegression.this.type
Suggested depth for treeAggregate (greater than or equal to 2).
Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

Annotations
@Since( "2.1.0" )
final def setDefault(paramPairs: ParamPair[_]*): LogisticRegression.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
def setElasticNetParam(value: Double): LogisticRegression.this.type
Set the ElasticNet mixing parameter.
Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.

Annotations
@Since( "1.4.0" )
def setFamily(value: String): LogisticRegression.this.type
Sets the value of param family.
Sets the value of param family. Default is "auto".

Annotations
@Since( "2.1.0" )
def setFeaturesCol(value: String): LogisticRegression

Definition Classes
Predictor
def setFitIntercept(value: Boolean): LogisticRegression.this.type
Whether to fit an intercept term.
Whether to fit an intercept term. Default is true.

Annotations
@Since( "1.4.0" )
def setLabelCol(value: String): LogisticRegression

Definition Classes
Predictor
def setLowerBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type
Set the lower bounds on coefficients if fitting under bound constrained optimization.
Set the lower bounds on coefficients if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setLowerBoundsOnIntercepts(value: Vector): LogisticRegression.this.type
Set the lower bounds on intercepts if fitting under bound constrained optimization.
Set the lower bounds on intercepts if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setMaxBlockSizeInMB(value: Double): LogisticRegression.this.type
Sets the value of param maxBlockSizeInMB.
Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

Annotations
@Since( "3.1.0" )
def setMaxIter(value: Int): LogisticRegression.this.type
Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.

Annotations
@Since( "1.2.0" )
def setPredictionCol(value: String): LogisticRegression

Definition Classes
Predictor
def setProbabilityCol(value: String): LogisticRegression

Definition Classes
ProbabilisticClassifier
def setRawPredictionCol(value: String): LogisticRegression

Definition Classes
Classifier
def setRegParam(value: Double): LogisticRegression.this.type
Set the regularization parameter.
Set the regularization parameter. Default is 0.0.

Annotations
@Since( "1.2.0" )
def setStandardization(value: Boolean): LogisticRegression.this.type
Whether to standardize the training features before fitting the model.
Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

Annotations
@Since( "1.5.0" )
def setThreshold(value: Double): LogisticRegression.this.type
Set threshold in binary classification, in range [0, 1].
Set threshold in binary classification, in range [0, 1].
If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.
Default is 0.5.

Definition Classes
LogisticRegression → LogisticRegressionParams
Annotations
@Since( "1.5.0" )
def setThresholds(value: Array[Double]): LogisticRegression.this.type
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

Definition Classes
LogisticRegression → LogisticRegressionParams → ProbabilisticClassifier
Annotations
@Since( "1.5.0" )
def setTol(value: Double): LogisticRegression.this.type
Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

Annotations
@Since( "1.4.0" )
def setUpperBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type
Set the upper bounds on coefficients if fitting under bound constrained optimization.
Set the upper bounds on coefficients if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setUpperBoundsOnIntercepts(value: Vector): LogisticRegression.this.type
Set the upper bounds on intercepts if fitting under bound constrained optimization.
Set the upper bounds on intercepts if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setWeightCol(value: String): LogisticRegression.this.type
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

Annotations
@Since( "1.6.0" )
final val standardization: BooleanParam
Param for whether to standardize the training features before fitting the model.
Param for whether to standardize the training features before fitting the model.

Definition Classes
HasStandardization
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val threshold: DoubleParam
Param for threshold in binary classification prediction, in range [0, 1].
Param for threshold in binary classification prediction, in range [0, 1].

Definition Classes
HasThreshold
val thresholds: DoubleArrayParam
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Definition Classes
HasThresholds
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).

Definition Classes
HasTol
def train(dataset: Dataset[_]): LogisticRegressionModel
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.
dataset
Training dataset
returns
Fitted model

Attributes
protected[spark]
Definition Classes
LogisticRegression → Predictor
def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
Predictor → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.

Definition Classes
LogisticRegression → Identifiable
Annotations
@Since( "1.4.0" )
val upperBoundsOnCoefficients: Param[Matrix]
The upper bounds on coefficients if fitting under bound constrained optimization.
The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val upperBoundsOnIntercepts: Param[Vector]
The upper bounds on intercepts if fitting under bound constrained optimization.
The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def usingBoundConstrainedOptimization: Boolean

Attributes
protected
Definition Classes
LogisticRegressionParams
def validateAndTransformSchema(schema: StructType, 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
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
def validateLabel(label: Double, numClasses: Int): Unit
Validates the label on the classifier is a valid integer in the range [0, numClasses).
Validates the label on the classifier is a valid integer in the range [0, numClasses).
label
The label to validate.
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
def validateNumClasses(numClasses: Int): Unit
Validates that number of classes is greater than zero.
Validates that number of classes is greater than zero.
numClasses
Number of classes label can take.

Attributes
protected
Definition Classes
Classifier
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( ... ) @native()
final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Definition Classes
HasWeightCol
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Value Members

def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Inherited from MLWritable

Value Members

def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )

Inherited from LogisticRegressionParams

Value Members

def checkThresholdConsistency(): Unit
If threshold and thresholds are both set, ensures they are consistent.
If threshold and thresholds are both set, ensures they are consistent.

Attributes
protected
Definition Classes
LogisticRegressionParams
Exceptions thrown
IllegalArgumentException if threshold and thresholds are not equivalent
final val family: Param[String]
Param for the name of family which is a description of the label distribution to be used in the model.
Param for the name of family which is a description of the label distribution to be used in the model. Supported options:
- "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
- "binomial": Binary logistic regression with pivoting.
- "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".
Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
def getFamily: String

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
def getLowerBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getLowerBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getUpperBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getUpperBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val lowerBoundsOnCoefficients: Param[Matrix]
The lower bounds on coefficients if fitting under bound constrained optimization.
The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val lowerBoundsOnIntercepts: Param[Vector]
The lower bounds on intercepts if fitting under bound constrained optimization.
The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val upperBoundsOnCoefficients: Param[Matrix]
The upper bounds on coefficients if fitting under bound constrained optimization.
The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val upperBoundsOnIntercepts: Param[Vector]
The upper bounds on intercepts if fitting under bound constrained optimization.
The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def usingBoundConstrainedOptimization: Boolean

Attributes
protected
Definition Classes
LogisticRegressionParams
def validateAndTransformSchema(schema: StructType, 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
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams

Inherited from HasMaxBlockSizeInMB

Value Members

final def getMaxBlockSizeInMB: Double

Definition Classes
HasMaxBlockSizeInMB
final val maxBlockSizeInMB: DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.
Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

Definition Classes
HasMaxBlockSizeInMB

Inherited from HasAggregationDepth

Value Members

final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).

Definition Classes
HasAggregationDepth
final def getAggregationDepth: Int

Definition Classes
HasAggregationDepth

Inherited from HasThreshold

Value Members

val threshold: DoubleParam
Param for threshold in binary classification prediction, in range [0, 1].
Param for threshold in binary classification prediction, in range [0, 1].

Definition Classes
HasThreshold

Inherited from HasWeightCol

Value Members

final def getWeightCol: String

Definition Classes
HasWeightCol
final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Definition Classes
HasWeightCol

Inherited from HasStandardization

Value Members

final def getStandardization: Boolean

Definition Classes
HasStandardization
final val standardization: BooleanParam
Param for whether to standardize the training features before fitting the model.
Param for whether to standardize the training features before fitting the model.

Definition Classes
HasStandardization

Inherited from HasTol

Value Members

final def getTol: Double

Definition Classes
HasTol
final val tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).

Definition Classes
HasTol

Inherited from HasFitIntercept

Value Members

final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.

Definition Classes
HasFitIntercept
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept

Inherited from HasMaxIter

Value Members

final def getMaxIter: Int

Definition Classes
HasMaxIter
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).

Definition Classes
HasMaxIter

Inherited from HasElasticNetParam

Value Members

final val elasticNetParam: DoubleParam
Param for the ElasticNet mixing parameter, in range [0, 1].
Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

Definition Classes
HasElasticNetParam
final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam

Inherited from HasRegParam

Value Members

final def getRegParam: Double

Definition Classes
HasRegParam
final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).

Definition Classes
HasRegParam

Inherited from ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

def setProbabilityCol(value: String): LogisticRegression

Definition Classes
ProbabilisticClassifier

Inherited from HasThresholds

Value Members

val thresholds: DoubleArrayParam
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Definition Classes
HasThresholds

Inherited from HasProbabilityCol

Value Members

final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol

Inherited from Classifier[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
dataset
DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
Note
Throws SparkException if any label is a non-integer or is negative
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int
Get the number of classes.
Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.
Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().
dataset
Dataset which contains a column labelCol
maxNumClasses
Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.
returns
number of classes

Attributes
protected
Definition Classes
Classifier
Exceptions thrown
IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses
def setRawPredictionCol(value: String): LogisticRegression

Definition Classes
Classifier
def validateLabel(label: Double, numClasses: Int): Unit
Validates the label on the classifier is a valid integer in the range [0, numClasses).
Validates the label on the classifier is a valid integer in the range [0, numClasses).
label
The label to validate.
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
def validateNumClasses(numClasses: Int): Unit
Validates that number of classes is greater than zero.
Validates that number of classes is greater than zero.
numClasses
Number of classes label can take.

Attributes
protected
Definition Classes
Classifier

Inherited from ClassifierParams

Value Members

def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

Attributes
protected
Definition Classes
ClassifierParams

Inherited from HasRawPredictionCol

Value Members

final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final val rawPredictionCol: Param[String]
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.

Definition Classes
HasRawPredictionCol

Inherited from Predictor[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
Predictor
def fit(dataset: Dataset[_]): LogisticRegressionModel
Fits a model to the input data.
Fits a model to the input data.

Definition Classes
Predictor → Estimator
def setFeaturesCol(value: String): LogisticRegression

Definition Classes
Predictor
def setLabelCol(value: String): LogisticRegression

Definition Classes
Predictor
def setPredictionCol(value: String): LogisticRegression

Definition Classes
Predictor
def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
Predictor → PipelineStage

Inherited from PredictorParams

Value Members

def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams

Inherited from HasPredictionCol

Value Members

final def getPredictionCol: String

Definition Classes
HasPredictionCol
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol

Inherited from HasFeaturesCol

Value Members

final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol

Inherited from HasLabelCol

Value Members

final def getLabelCol: String

Definition Classes
HasLabelCol
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol

Inherited from Estimator[LogisticRegressionModel]

Value Members

def fit(dataset: Dataset[_], paramMaps: Seq[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 override this to optimize multi-model training.
dataset
input dataset
paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted models, matching the input parameter maps

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], 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. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: 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
firstParamPair
the first param pair, overrides embedded params
otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()

Inherited from PipelineStage

Value Members

def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()

Inherited from Logging

Value Members

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
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

Inherited from Params

Value Members

final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def clear(param: Param[_]): LogisticRegression.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
param
input param, must belong to this instance.
returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value

Definition Classes
Params
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Definition Classes
Params
final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.

Definition Classes
Params
final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.

Definition Classes
Params
final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Definition Classes
Params
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.

Definition Classes
Params
def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.

Definition Classes
Params
final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.

Definition Classes
Params
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

Definition Classes
Params
Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
final def set(paramPair: ParamPair[_]): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): LogisticRegression.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): LogisticRegression.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params

Inherited from Identifiable

Value Members

def toString(): String

Definition Classes
Identifiable → AnyRef → Any

Inherited from AnyRef

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
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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( ... ) @native()

Inherited from Any

Value Members

final def asInstanceOf[T0]: T0

Definition Classes
Any
final def isInstanceOf[T0]: Boolean

Definition Classes
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.

final val elasticNetParam: DoubleParam
Param for the ElasticNet mixing parameter, in range [0, 1].
Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

Definition Classes
HasElasticNetParam
final val family: Param[String]
Param for the name of family which is a description of the label distribution to be used in the model.
Param for the name of family which is a description of the label distribution to be used in the model. Supported options:
- "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
- "binomial": Binary logistic regression with pivoting.
- "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".
Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.

Definition Classes
HasFitIntercept
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).

Definition Classes
HasMaxIter
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol
final val rawPredictionCol: Param[String]
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.

Definition Classes
HasRawPredictionCol
final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).

Definition Classes
HasRegParam
final val standardization: BooleanParam
Param for whether to standardize the training features before fitting the model.
Param for whether to standardize the training features before fitting the model.

Definition Classes
HasStandardization
val threshold: DoubleParam
Param for threshold in binary classification prediction, in range [0, 1].
Param for threshold in binary classification prediction, in range [0, 1].

Definition Classes
HasThreshold
val thresholds: DoubleArrayParam
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Definition Classes
HasThresholds
final val tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).

Definition Classes
HasTol
final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Definition Classes
HasWeightCol

Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def checkThresholdConsistency(): Unit
If threshold and thresholds are both set, ensures they are consistent.
If threshold and thresholds are both set, ensures they are consistent.

Attributes
protected
Definition Classes
LogisticRegressionParams
Exceptions thrown
IllegalArgumentException if threshold and thresholds are not equivalent
final def clear(param: Param[_]): LogisticRegression.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): LogisticRegression
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Definition Classes
LogisticRegression → Predictor → Estimator → PipelineStage → Params
Annotations
@Since( "1.4.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
param
input param, must belong to this instance.
returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value

Definition Classes
Params
def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See explainParam().

Definition Classes
Params
def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

Attributes
protected
Definition Classes
ClassifierParams
def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
dataset
DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
Note
Throws SparkException if any label is a non-integer or is negative
def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
Predictor
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with no extra values.

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Definition Classes
Params
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): LogisticRegressionModel
Fits a model to the input data.
Fits a model to the input data.

Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Seq[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 override this to optimize multi-model training.
dataset
input dataset
paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted models, matching the input parameter maps

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], 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. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: 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
firstParamPair
the first param pair, overrides embedded params
otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
returns
fitted model

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.

Definition Classes
Params
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.

Definition Classes
Params
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int
Get the number of classes.
Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.
Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().
dataset
Dataset which contains a column labelCol
maxNumClasses
Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.
returns
number of classes

Attributes
protected
Definition Classes
Classifier
Exceptions thrown
IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses
final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Definition Classes
Params
def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.

Definition Classes
Params
final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.

Definition Classes
Params
def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

Definition Classes
Params
Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): LogisticRegression.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): LogisticRegression.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): LogisticRegression.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_]): LogisticRegressionModel
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.
dataset
Training dataset
returns
Fitted model

Attributes
protected[spark]
Definition Classes
LogisticRegression → Predictor
def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
Predictor → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.

Definition Classes
LogisticRegression → Identifiable
Annotations
@Since( "1.4.0" )
def usingBoundConstrainedOptimization: Boolean

Attributes
protected
Definition Classes
LogisticRegressionParams
def validateAndTransformSchema(schema: StructType, 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
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
def validateLabel(label: Double, numClasses: Int): Unit
Validates the label on the classifier is a valid integer in the range [0, numClasses).
Validates the label on the classifier is a valid integer in the range [0, numClasses).
label
The label to validate.
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
Definition Classes
Classifier
def validateNumClasses(numClasses: Int): Unit
Validates that number of classes is greater than zero.
Validates that number of classes is greater than zero.
numClasses
Number of classes label can take.

Attributes
protected
Definition Classes
Classifier
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( ... ) @native()
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
DefaultParamsWritable → MLWritable

Parameter setters

def setElasticNetParam(value: Double): LogisticRegression.this.type
Set the ElasticNet mixing parameter.
Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.

Annotations
@Since( "1.4.0" )
def setFamily(value: String): LogisticRegression.this.type
Sets the value of param family.
Sets the value of param family. Default is "auto".

Annotations
@Since( "2.1.0" )
def setFeaturesCol(value: String): LogisticRegression

Definition Classes
Predictor
def setFitIntercept(value: Boolean): LogisticRegression.this.type
Whether to fit an intercept term.
Whether to fit an intercept term. Default is true.

Annotations
@Since( "1.4.0" )
def setLabelCol(value: String): LogisticRegression

Definition Classes
Predictor
def setMaxIter(value: Int): LogisticRegression.this.type
Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.

Annotations
@Since( "1.2.0" )
def setPredictionCol(value: String): LogisticRegression

Definition Classes
Predictor
def setProbabilityCol(value: String): LogisticRegression

Definition Classes
ProbabilisticClassifier
def setRawPredictionCol(value: String): LogisticRegression

Definition Classes
Classifier
def setRegParam(value: Double): LogisticRegression.this.type
Set the regularization parameter.
Set the regularization parameter. Default is 0.0.

Annotations
@Since( "1.2.0" )
def setStandardization(value: Boolean): LogisticRegression.this.type
Whether to standardize the training features before fitting the model.
Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

Annotations
@Since( "1.5.0" )
def setThreshold(value: Double): LogisticRegression.this.type
Set threshold in binary classification, in range [0, 1].
Set threshold in binary classification, in range [0, 1].
If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.
Default is 0.5.

Definition Classes
LogisticRegression → LogisticRegressionParams
Annotations
@Since( "1.5.0" )
def setThresholds(value: Array[Double]): LogisticRegression.this.type
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

Definition Classes
LogisticRegression → LogisticRegressionParams → ProbabilisticClassifier
Annotations
@Since( "1.5.0" )
def setTol(value: Double): LogisticRegression.this.type
Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

Annotations
@Since( "1.4.0" )
def setWeightCol(value: String): LogisticRegression.this.type
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

Annotations
@Since( "1.6.0" )

Parameter getters

final def getElasticNetParam: Double

Definition Classes
HasElasticNetParam
def getFamily: String

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.1.0" )
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getMaxIter: Int

Definition Classes
HasMaxIter
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final def getRegParam: Double

Definition Classes
HasRegParam
final def getStandardization: Boolean

Definition Classes
HasStandardization
def getThreshold: Double
Get threshold for binary classification.
Get threshold for binary classification.
If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:
```
1 / (1 + thresholds(0) / thresholds(1))
```
. Otherwise, returns threshold if set, or its default value if unset.
1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold if set, or its default value if unset.
Definition Classes
LogisticRegression → LogisticRegressionParams → HasThreshold
Annotations
@Since( "1.5.0" )
Exceptions thrown
IllegalArgumentException if thresholds is set to an array of length other than 2.
def getThresholds: Array[Double]
Get thresholds for binary or multiclass classification.
Get thresholds for binary or multiclass classification.
If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

Definition Classes
LogisticRegression → LogisticRegressionParams → HasThresholds
Annotations
@Since( "1.5.0" )
final def getTol: Double

Definition Classes
HasTol
final def getWeightCol: String

Definition Classes
HasWeightCol

(expert-only) Parameters

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

final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).

Definition Classes
HasAggregationDepth
val lowerBoundsOnCoefficients: Param[Matrix]
The lower bounds on coefficients if fitting under bound constrained optimization.
The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val lowerBoundsOnIntercepts: Param[Vector]
The lower bounds on intercepts if fitting under bound constrained optimization.
The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
final val maxBlockSizeInMB: DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.
Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

Definition Classes
HasMaxBlockSizeInMB
val upperBoundsOnCoefficients: Param[Matrix]
The upper bounds on coefficients if fitting under bound constrained optimization.
The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
val upperBoundsOnIntercepts: Param[Vector]
The upper bounds on intercepts if fitting under bound constrained optimization.
The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )

(expert-only) Parameter setters

def setAggregationDepth(value: Int): LogisticRegression.this.type
Suggested depth for treeAggregate (greater than or equal to 2).
Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

Annotations
@Since( "2.1.0" )
def setLowerBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type
Set the lower bounds on coefficients if fitting under bound constrained optimization.
Set the lower bounds on coefficients if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setLowerBoundsOnIntercepts(value: Vector): LogisticRegression.this.type
Set the lower bounds on intercepts if fitting under bound constrained optimization.
Set the lower bounds on intercepts if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setMaxBlockSizeInMB(value: Double): LogisticRegression.this.type
Sets the value of param maxBlockSizeInMB.
Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

Annotations
@Since( "3.1.0" )
def setUpperBoundsOnCoefficients(value: Matrix): LogisticRegression.this.type
Set the upper bounds on coefficients if fitting under bound constrained optimization.
Set the upper bounds on coefficients if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )
def setUpperBoundsOnIntercepts(value: Vector): LogisticRegression.this.type
Set the upper bounds on intercepts if fitting under bound constrained optimization.
Set the upper bounds on intercepts if fitting under bound constrained optimization.

Annotations
@Since( "2.2.0" )

(expert-only) Parameter getters

final def getAggregationDepth: Int

Definition Classes
HasAggregationDepth
def getLowerBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getLowerBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
final def getMaxBlockSizeInMB: Double

Definition Classes
HasMaxBlockSizeInMB
def getUpperBoundsOnCoefficients: Matrix

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )
def getUpperBoundsOnIntercepts: Vector

Definition Classes
LogisticRegressionParams
Annotations
@Since( "2.2.0" )

Packages

LogisticRegression 

Companion object LogisticRegression

class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams with DefaultParamsWritable with Logging

Instance Constructors

Value Members

Inherited from DefaultParamsWritable

Value Members

Inherited from MLWritable

Value Members

Inherited from LogisticRegressionParams

Value Members

Inherited from HasMaxBlockSizeInMB

Value Members

Inherited from HasAggregationDepth

Value Members

Inherited from HasThreshold

Value Members

Inherited from HasWeightCol

Value Members

Inherited from HasStandardization

Value Members

Inherited from HasTol

Value Members

Inherited from HasFitIntercept

Value Members

Inherited from HasMaxIter

Value Members

Inherited from HasElasticNetParam

Value Members

Inherited from HasRegParam

Value Members

Inherited from ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

Inherited from HasThresholds

Value Members

Inherited from HasProbabilityCol

Value Members

Inherited from Classifier[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

Inherited from ClassifierParams

Value Members

Inherited from HasRawPredictionCol

Value Members

Inherited from Predictor[Vector, LogisticRegression, LogisticRegressionModel]

Value Members

Inherited from PredictorParams

Value Members

Inherited from HasPredictionCol

Value Members

Inherited from HasFeaturesCol

Value Members

Inherited from HasLabelCol

Value Members

Inherited from Estimator[LogisticRegressionModel]

Value Members

Inherited from PipelineStage

Value Members

Inherited from Logging

Value Members

Inherited from Params

Value Members

Inherited from Identifiable

Value Members

Inherited from AnyRef

Value Members

Inherited from Any

Value Members

Parameters

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter setters

(expert-only) Parameter getters

LogisticRegression