Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/19020#discussion_r139765034 --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala --- @@ -69,19 +69,57 @@ private[regression] trait LinearRegressionParams extends PredictorParams "The solver algorithm for optimization. Supported options: " + s"${supportedSolvers.mkString(", ")}. (Default auto)", ParamValidators.inArray[String](supportedSolvers)) + + /** + * The loss function to be optimized. + * Supported options: "leastSquares" and "huber". + * Default: "leastSquares" + * + * @group param + */ + @Since("2.3.0") + final override val loss: Param[String] = new Param[String](this, "loss", "The loss function to" + + s" be optimized. Supported options: ${supportedLosses.mkString(", ")}. (Default leastSquares)", + ParamValidators.inArray[String](supportedLosses)) + + /** + * 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. + * Only valid when "loss" is "huber". + */ + @Since("2.3.0") + final val epsilon = new DoubleParam(this, "epsilon", "The shape parameter to control the " + --- End diff -- Mark as expertParam (same for set/get)
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