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| author | Yanbo Liang <ybliang8@gmail.com> | 2016-05-27 13:16:22 -0700 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2016-05-27 13:16:22 -0700 |
| commit | a3550e3747e21c79a5110132dc127ee83879062a (patch) | |
| tree | 23e2aed72732e30d4192c171dbd5aa3f5cb6fe02 /mllib/src | |
| parent | c96244f5acd8b335e34694c171bab32d92e6e0fb (diff) | |
| download | spark-a3550e3747e21c79a5110132dc127ee83879062a.tar.gz spark-a3550e3747e21c79a5110132dc127ee83879062a.tar.bz2 spark-a3550e3747e21c79a5110132dc127ee83879062a.zip | |
[SPARK-11959][SPARK-15484][DOC][ML] Document WLS and IRLS
## What changes were proposed in this pull request?
* Document ```WeightedLeastSquares```(normal equation) and ```IterativelyReweightedLeastSquares```.
* Copy ```L-BFGS``` documents from ```spark.mllib``` to ```spark.ml```.
Due to the session ```Optimization of linear methods``` is used for developers, I think we should provide the brief introduction of the optimization method, necessary references and how it implements in Spark. It's not necessary to paste all mathematical formula and derivation here. If developers/users want to learn more, they can track reference.
## How was this patch tested?
Document update, no tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #13262 from yanboliang/spark-15484.
Diffstat (limited to 'mllib/src')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala index 6ed193cf57..d732f53029 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala @@ -38,7 +38,7 @@ private[ml] class IterativelyReweightedLeastSquaresModel( /** * Implements the method of iteratively reweighted least squares (IRLS) which is used to solve * certain optimization problems by an iterative method. In each step of the iterations, it - * involves solving a weighted lease squares (WLS) problem by [[WeightedLeastSquares]]. + * involves solving a weighted least squares (WLS) problem by [[WeightedLeastSquares]]. * It can be used to find maximum likelihood estimates of a generalized linear model (GLM), * find M-estimator in robust regression and other optimization problems. * |