diff options
| author | Yanbo Liang <ybliang8@gmail.com> | 2015-06-16 14:30:30 -0700 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2015-06-16 14:30:42 -0700 |
| commit | 15d973f2d9c2512dd5a882b6b65fb494de526643 (patch) | |
| tree | 65f3746cdbec3e375a0640ef82db5308b50e9a1a /mllib/src/main | |
| parent | b9e5d3cadd0f07c211623b045466220c39abdc56 (diff) | |
| download | spark-15d973f2d9c2512dd5a882b6b65fb494de526643.tar.gz spark-15d973f2d9c2512dd5a882b6b65fb494de526643.tar.bz2 spark-15d973f2d9c2512dd5a882b6b65fb494de526643.zip | |
[SPARK-7916] [MLLIB] MLlib Python doc parity check for classification and regression
Check then make the MLlib Python classification and regression doc to be as complete as the Scala doc.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #6460 from yanboliang/spark-7916 and squashes the following commits:
f8deda4 [Yanbo Liang] trigger jenkins
6dc4d99 [Yanbo Liang] address comments
ce2a43e [Yanbo Liang] truncate too long line and remove extra sparse
3eaf6ad [Yanbo Liang] MLlib Python doc parity check for classification and regression
(cherry picked from commit ca998757e8ff2bdca2c7e88055c389161521d604)
Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
Diffstat (limited to 'mllib/src/main')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index e0c03d8180..7d28ffad45 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -73,7 +73,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] { /** * Train a regression model with L2-regularization using Stochastic Gradient Descent. - * This solves the l1-regularized least squares regression formulation + * This solves the l2-regularized least squares regression formulation * f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^ * Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with * its corresponding right hand side label y. |