Packages

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

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Inherited
  1. LogisticRegressionModel
  2. HasTrainingSummary
  3. LogisticRegressionParams
  4. HasMaxBlockSizeInMB
  5. HasAggregationDepth
  6. HasThreshold
  7. HasWeightCol
  8. HasStandardization
  9. HasTol
  10. HasFitIntercept
  11. HasMaxIter
  12. HasElasticNetParam
  13. HasRegParam
  14. MLWritable
  15. ProbabilisticClassificationModel
  16. ProbabilisticClassifierParams
  17. HasThresholds
  18. HasProbabilityCol
  19. ClassificationModel
  20. ClassifierParams
  21. HasRawPredictionCol
  22. PredictionModel
  23. PredictorParams
  24. HasPredictionCol
  25. HasFeaturesCol
  26. HasLabelCol
  27. Model
  28. Transformer
  29. PipelineStage
  30. Logging
  31. Params
  32. Serializable
  33. Serializable
  34. Identifiable
  35. AnyRef
  36. Any
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Parameters

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

  1. 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
  2. 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" )
  3. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  4. final val fitIntercept: BooleanParam

    Param for whether to fit an intercept term.

    Param for whether to fit an intercept term.

    Definition Classes
    HasFitIntercept
  5. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  6. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  7. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  8. 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
  9. 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
  10. final val regParam: DoubleParam

    Param for regularization parameter (>= 0).

    Param for regularization parameter (>= 0).

    Definition Classes
    HasRegParam
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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

  1. 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" )
  2. 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
  3. val coefficientMatrix: Matrix
    Annotations
    @Since( "2.1.0" )
  4. 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" )
  5. 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
    LogisticRegressionModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  6. 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" )
  7. 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
  8. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  9. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  10. 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
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  18. 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" )
  19. 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" )
  20. val interceptVector: Vector
    Annotations
    @Since( "2.1.0" )
  21. 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
  22. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  23. val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes
    LogisticRegressionModelClassificationModel
    Annotations
    @Since( "1.3.0" )
  24. 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
    LogisticRegressionModelPredictionModel
    Annotations
    @Since( "1.6.0" )
  25. 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.

  26. 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.

  27. 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
    LogisticRegressionModelClassificationModelPredictionModel
  28. 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" )
  29. 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
    LogisticRegressionModelClassificationModel
    Annotations
    @Since( "3.0.0" )
  30. 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( ... )
  31. 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
  32. 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
  33. 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" )
  34. def toString(): String
    Definition Classes
    LogisticRegressionModelIdentifiable → AnyRef → Any
  35. 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:

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    ProbabilisticClassificationModelClassificationModelPredictionModelTransformer
  36. 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" )
  37. 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()
  38. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModelPredictionModel
  39. 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
    ProbabilisticClassificationModelClassificationModelPredictionModelPipelineStage
  40. 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
    LogisticRegressionModelIdentifiable
    Annotations
    @Since( "1.4.0" )
  41. 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
    LogisticRegressionModelMLWritable
    Annotations
    @Since( "1.6.0" )

Parameter setters

  1. def setFeaturesCol(value: String): LogisticRegressionModel

    Definition Classes
    PredictionModel
  2. def setPredictionCol(value: String): LogisticRegressionModel

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

  4. def setRawPredictionCol(value: String): LogisticRegressionModel

    Definition Classes
    ClassificationModel
  5. 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" )
  6. 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" )

Parameter getters

  1. final def getElasticNetParam: Double

    Definition Classes
    HasElasticNetParam
  2. def getFamily: String

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

    Definition Classes
    HasFeaturesCol
  4. final def getFitIntercept: Boolean

    Definition Classes
    HasFitIntercept
  5. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  6. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  7. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  8. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  9. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  10. final def getRegParam: Double

    Definition Classes
    HasRegParam
  11. final def getStandardization: Boolean

    Definition Classes
    HasStandardization
  12. 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.

  13. 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" )
  14. final def getTol: Double

    Definition Classes
    HasTol
  15. 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.

  1. final val aggregationDepth: IntParam

    Param for suggested depth for treeAggregate (>= 2).

    Param for suggested depth for treeAggregate (>= 2).

    Definition Classes
    HasAggregationDepth
  2. 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" )
  3. 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" )
  4. 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
  5. 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" )
  6. 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 getters

  1. final def getAggregationDepth: Int

    Definition Classes
    HasAggregationDepth
  2. def getLowerBoundsOnCoefficients: Matrix

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

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

    Definition Classes
    HasMaxBlockSizeInMB
  5. def getUpperBoundsOnCoefficients: Matrix

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

    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )