LogisticRegressionModel

Companion object LogisticRegressionModel

class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with MLWritable with LogisticRegressionParams with HasTrainingSummary[LogisticRegressionTrainingSummary]

Annotations: @Since( "1.4.0" )
Source: LogisticRegression.scala

Linear Supertypes

HasTrainingSummary[LogisticRegressionTrainingSummary], LogisticRegressionParams, HasMaxBlockSizeInMB, HasAggregationDepth, HasThreshold, HasWeightCol, HasStandardization, HasTol, HasFitIntercept, HasMaxIter, HasElasticNetParam, HasRegParam, MLWritable, ProbabilisticClassificationModel[Vector, LogisticRegressionModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, LogisticRegressionModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, LogisticRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[LogisticRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

Ordering

Grouped
Alphabetic
By Inheritance

Inherited

LogisticRegressionModel
HasTrainingSummary
LogisticRegressionParams
HasMaxBlockSizeInMB
HasAggregationDepth
HasThreshold
HasWeightCol
HasStandardization
HasTol
HasFitIntercept
HasMaxIter
HasElasticNetParam
HasRegParam
MLWritable
ProbabilisticClassificationModel
ProbabilisticClassifierParams
HasThresholds
HasProbabilityCol
ClassificationModel
ClassifierParams
HasRawPredictionCol
PredictionModel
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Model
Transformer
PipelineStage
Logging
Params
Serializable
Serializable
Identifiable
AnyRef
Any

Hide All
Show All

Visibility

Public
All

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 binarySummary: BinaryLogisticRegressionTrainingSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.

Annotations
@Since( "2.3.0" )
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[_]): LogisticRegressionModel.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()
val coefficientMatrix: Matrix

Annotations
@Since( "2.1.0" )
def coefficients: Vector
A vector of model coefficients for "binomial" logistic regression.
A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.
returns
Vector

Annotations
@Since( "2.0.0" )
def copy(extra: ParamMap): LogisticRegressionModel
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
LogisticRegressionModel → Model → Transformer → 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 evaluate(dataset: Dataset[_]): LogisticRegressionSummary
Evaluates the model on a test dataset.
Evaluates the model on a test dataset.
dataset
Test dataset to evaluate model on.

Annotations
@Since( "2.0.0" )
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
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 featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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
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
LogisticRegressionModel → 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
LogisticRegressionModel → 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 hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
def hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.

Definition Classes
HasTrainingSummary
Annotations
@Since( "3.0.0" )
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
def intercept: Double
The model intercept for "binomial" logistic regression.
The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.
returns
Double

Annotations
@Since( "1.3.0" )
val interceptVector: Vector

Annotations
@Since( "2.1.0" )
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()
val numClasses: Int
Number of classes (values which the label can take).
Number of classes (values which the label can take).

Definition Classes
LogisticRegressionModel → ClassificationModel
Annotations
@Since( "1.3.0" )
val numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1

Definition Classes
LogisticRegressionModel → PredictionModel
Annotations
@Since( "1.6.0" )
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.
var parent: Estimator[LogisticRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def predict(features: Vector): Double
Predict label for the given feature vector.
Predict label for the given feature vector. The behavior of this can be adjusted using thresholds.

Definition Classes
LogisticRegressionModel → ClassificationModel → PredictionModel
def predictProbability(features: Vector): Vector
Predict the probability of each class given the features.
Predict the probability of each class given the features. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
returns
Estimated class conditional probabilities

Definition Classes
ProbabilisticClassificationModel
Annotations
@Since( "3.0.0" )
def predictRaw(features: Vector): Vector
Raw prediction for each possible label.
Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.
returns
vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

Definition Classes
LogisticRegressionModel → ClassificationModel
Annotations
@Since( "3.0.0" )
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
def probability2prediction(probability: Vector): Double
Given a vector of class conditional probabilities, select the predicted label.
Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.
returns
predicted label

Attributes
protected
Definition Classes
LogisticRegressionModel → ProbabilisticClassificationModel
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
def raw2prediction(rawPrediction: Vector): Double
Given a vector of raw predictions, select the predicted label.
Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.
returns
predicted label

Attributes
protected
Definition Classes
LogisticRegressionModel → ProbabilisticClassificationModel → ClassificationModel
def raw2probability(rawPrediction: Vector): Vector
Non-in-place version of raw2probabilityInPlace()
Non-in-place version of raw2probabilityInPlace()

Attributes
protected
Definition Classes
ProbabilisticClassificationModel
def raw2probabilityInPlace(rawPrediction: Vector): Vector
Estimate the probability of each class given the raw prediction, doing the computation in-place.
Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
returns
Estimated class conditional probabilities (modified input vector)

Attributes
protected
Definition Classes
LogisticRegressionModel → ProbabilisticClassificationModel
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[_]): LogisticRegressionModel.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): LogisticRegressionModel.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): LogisticRegressionModel.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[_]*): LogisticRegressionModel.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): LogisticRegressionModel.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[ml]
Definition Classes
Params
def setFeaturesCol(value: String): LogisticRegressionModel

Definition Classes
PredictionModel
def setParent(parent: Estimator[LogisticRegressionModel]): LogisticRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

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

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

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

Definition Classes
ClassificationModel
def setThreshold(value: Double): LogisticRegressionModel.this.type
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
LogisticRegressionModel → LogisticRegressionParams
Annotations
@Since( "1.5.0" )
def setThresholds(value: Array[Double]): LogisticRegressionModel.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
LogisticRegressionModel → LogisticRegressionParams → ProbabilisticClassificationModel
Annotations
@Since( "1.5.0" )
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
def summary: LogisticRegressionTrainingSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if hasSummary is false.

Definition Classes
LogisticRegressionModel → HasTrainingSummary
Annotations
@Since( "1.5.0" )
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
LogisticRegressionModel → 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 transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector.
dataset
input dataset
returns
transformed dataset

Definition Classes
ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
final def transformImpl(dataset: Dataset[_]): DataFrame

Definition Classes
ClassificationModel → PredictionModel
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
ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
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
LogisticRegressionModel → 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
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 a org.apache.spark.ml.util.MLWriter instance for this ML instance.
Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.
For LogisticRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
This also does not save the parent currently.

Definition Classes
LogisticRegressionModel → MLWritable
Annotations
@Since( "1.6.0" )

Inherited from HasTrainingSummary[LogisticRegressionTrainingSummary]

Inherited from LogisticRegressionParams

Inherited from HasMaxBlockSizeInMB

Inherited from HasAggregationDepth

Inherited from HasThreshold

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasTol

Inherited from HasFitIntercept

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from MLWritable

Inherited from ProbabilisticClassificationModel[Vector, LogisticRegressionModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, LogisticRegressionModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, LogisticRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[LogisticRegressionModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

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

Members

Parameter setters

Parameter getters

(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.

Packages

LogisticRegressionModel 

Companion object LogisticRegressionModel

class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with MLWritable with LogisticRegressionParams with HasTrainingSummary[LogisticRegressionTrainingSummary]

Value Members

Inherited from HasTrainingSummary[LogisticRegressionTrainingSummary]

Inherited from LogisticRegressionParams

Inherited from HasMaxBlockSizeInMB

Inherited from HasAggregationDepth

Inherited from HasThreshold

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasTol

Inherited from HasFitIntercept

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from MLWritable

Inherited from ProbabilisticClassificationModel[Vector, LogisticRegressionModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, LogisticRegressionModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, LogisticRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[LogisticRegressionModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

LogisticRegressionModel