Package org.apache.spark.mllib.classification

Class LogisticRegressionModel

Object
org.apache.spark.mllib.regression.GeneralizedLinearModel
org.apache.spark.mllib.classification.LogisticRegressionModel
All Implemented Interfaces:
Serializable, ClassificationModel, PMMLExportable, Saveable, scala.Serializable
public class LogisticRegressionModel extends GeneralizedLinearModel implements ClassificationModel, scala.Serializable, Saveable, PMMLExportable
Classification model trained using Multinomial/Binary Logistic Regression.

param: weights Weights computed for every feature. param: intercept Intercept computed for this model. (Only used in Binary Logistic Regression. In Multinomial Logistic Regression, the intercepts will not be a single value, so the intercepts will be part of the weights.) param: numFeatures the dimension of the features. param: numClasses the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

See Also:

  • Constructor Details

    • LogisticRegressionModel

      public LogisticRegressionModel(Vector weights, double intercept, int numFeatures, int numClasses)

    • LogisticRegressionModel

      public LogisticRegressionModel(Vector weights, double intercept)
      Constructs a LogisticRegressionModel with weights and intercept for binary classification.
      Parameters:
      weights - (undocumented)
      intercept - (undocumented)

  • Method Details

    • load

      public static LogisticRegressionModel load(SparkContext sc, String path)

    • weights

      public Vector weights()
      Overrides:
      weights in class GeneralizedLinearModel

    • intercept

      public double intercept()
      Overrides:
      intercept in class GeneralizedLinearModel

    • numFeatures

      public int numFeatures()

    • numClasses

      public int numClasses()

    • setThreshold

      public LogisticRegressionModel setThreshold(double threshold)
      Sets the threshold that separates positive predictions from negative predictions in Binary Logistic Regression. An example with prediction score greater than or equal to this threshold is identified as a positive, and negative otherwise. The default value is 0.5. It is only used for binary classification.
      Parameters:
      threshold - (undocumented)
      Returns:
      (undocumented)

    • getThreshold

      public scala.Option<Object> getThreshold()
      Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. It is only used for binary classification.
      Returns:
      (undocumented)

    • clearThreshold

      public LogisticRegressionModel clearThreshold()
      Clears the threshold so that predict will output raw prediction scores. It is only used for binary classification.
      Returns:
      (undocumented)

    • save

      public void save(SparkContext sc, String path)
      Description copied from interface: Saveable
      Save this model to the given path.

      This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/

      The model may be loaded using Loader.load.

      Specified by:
      save in interface Saveable
      Parameters:
      sc - Spark context used to save model data.
      path - Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    • toString

      public String toString()
      Description copied from class: GeneralizedLinearModel
      Print a summary of the model.
      Overrides:
      toString in class GeneralizedLinearModel
      Returns:
      (undocumented)