Interface LinearRegressionParams

All Superinterfaces:
HasAggregationDepth, HasElasticNetParam, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasLoss, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasRegParam, HasSolver, HasStandardization, HasTol, HasWeightCol, Identifiable, Params, PredictorParams, Serializable, scala.Serializable
All Known Implementing Classes:
LinearRegression, LinearRegressionModel

Params for linear regression.
  • Method Details

    • epsilon

      DoubleParam epsilon()
      The shape parameter to control the amount of robustness. Must be > 1.0. At larger values of epsilon, the huber criterion becomes more similar to least squares regression; for small values of epsilon, the criterion is more similar to L1 regression. Default is 1.35 to get as much robustness as possible while retaining 95% statistical efficiency for normally distributed data. It matches sklearn HuberRegressor and is "M" from A robust hybrid of lasso and ridge regression. Only valid when "loss" is "huber".

    • getEpsilon

      double getEpsilon()
    • loss

      Param<String> loss()
      The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError"

      Specified by:
      loss in interface HasLoss
    • solver

      Param<String> solver()
      The solver algorithm for optimization. Supported options: "l-bfgs", "normal" and "auto". Default: "auto"

      Specified by:
      solver in interface HasSolver
    • validateAndTransformSchema

      StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
      Description copied from interface: PredictorParams
      Validates and transforms the input schema with the provided param map.

      Specified by:
      validateAndTransformSchema in interface PredictorParams
      schema - input schema
      fitting - whether this is in fitting
      featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
      output schema