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Diffstat (limited to 'docs/mllib-linear-methods.md')
| -rw-r--r-- | docs/mllib-linear-methods.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 63665c49bc..17d781ac23 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -185,10 +185,10 @@ algorithm for 200 iterations. import org.apache.spark.mllib.optimization.L1Updater val svmAlg = new SVMWithSGD() -svmAlg.optimizer. - setNumIterations(200). - setRegParam(0.1). - setUpdater(new L1Updater) +svmAlg.optimizer + .setNumIterations(200) + .setRegParam(0.1) + .setUpdater(new L1Updater) val modelL1 = svmAlg.run(training) {% endhighlight %} @@ -395,7 +395,7 @@ section of the Spark quick-start guide. Be sure to also include *spark-mllib* to your build file as a dependency. -###Streaming linear regression +### Streaming linear regression When data arrive in a streaming fashion, it is useful to fit regression models online, updating the parameters of the model as new data arrives. `spark.mllib` currently supports |