org.apache.spark.ml.classification
LogisticRegressionModel
Companion object LogisticRegressionModel
class LogisticRegressionModel extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams with MLWritable
- Grouped
- Alphabetic
- By Inheritance
- LogisticRegressionModel
- MLWritable
- LogisticRegressionParams
- HasAggregationDepth
- HasThreshold
- HasWeightCol
- HasStandardization
- HasTol
- HasFitIntercept
- HasMaxIter
- HasElasticNetParam
- HasRegParam
- 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
- 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
trainingSummary == None
or it is a multiclass model.- Annotations
- @Since( "2.3.0" )
-
def
checkThresholdConsistency(): Unit
If
threshold
andthresholds
are both set, ensures they are consistent.If
threshold
andthresholds
are both set, ensures they are consistent.- Attributes
- protected
- Definition Classes
- LogisticRegressionParams
- Exceptions thrown
IllegalArgumentException
ifthreshold
andthresholds
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[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
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 toparamMap
. 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
-
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[java.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
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
ifthresholds
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, ifthreshold
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.
-
def
hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.
- Annotations
- @Since( "1.5.0" )
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): 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
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
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
-
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.
- Attributes
- protected
- Definition Classes
- LogisticRegressionModel → ClassificationModel
-
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
- 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))
. WhensetThreshold()
is called, any user-set value forthresholds
will be cleared. If boththreshold
andthresholds
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 forthreshold
will be cleared. If boththreshold
andthresholds
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
trainingSummary == None
.- 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
-
final
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
- predicted labels as predictionCol of type
-
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()
-
def
transformImpl(dataset: Dataset[_]): DataFrame
- Attributes
- protected
- Definition Classes
- PredictionModel
-
def
transformSchema(schema: StructType): StructType
:: DeveloperApi ::
:: DeveloperApi ::
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- 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
- @native() @throws( ... )
-
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 MLWritable
Inherited from LogisticRegressionParams
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 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
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.