Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/4259#discussion_r29098031
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -42,34 +50,122 @@ private[regression] trait LinearRegressionParams
extends RegressorParams
class LinearRegression extends Regressor[Vector, LinearRegression,
LinearRegressionModel]
with LinearRegressionParams {
- setDefault(regParam -> 0.1, maxIter -> 100)
-
- /** @group setParam */
+ /**
+ * Set the regularization parameter.
+ * Default is 0.0.
+ * @group setParam
+ */
def setRegParam(value: Double): this.type = set(regParam, value)
+ setDefault(regParam -> 0.0)
--- End diff --
To match R's default result, we need to `0.0`. Also, the meaning of lambda
will be changed if the numbers of sample is changed. So it's hard to come out
with a good default. Why don't we implement regularization path to find the
best lambda?
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