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https://issues.apache.org/jira/browse/SPARK-5273?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14355058#comment-14355058
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Sandeep Narayanaswami commented on SPARK-5273:
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I can pick this up.
> Improve documentation examples for LinearRegression
> ----------------------------------------------------
>
> Key: SPARK-5273
> URL: https://issues.apache.org/jira/browse/SPARK-5273
> Project: Spark
> Issue Type: Improvement
> Components: Documentation
> Reporter: Dev Lakhani
> Priority: Minor
>
> In the document:
> https://spark.apache.org/docs/1.1.1/mllib-linear-methods.html
> Under
> Linear least squares, Lasso, and ridge regression
> The suggested method to use LinearRegressionWithSGD.train()
> // Building the model
> val numIterations = 100
> val model = LinearRegressionWithSGD.train(parsedData, numIterations)
> is not ideal even for simple examples such as y=x. This should be replaced
> with more real world parameters with step size:
> val lr = new LinearRegressionWithSGD()
> lr.optimizer.setStepSize(0.00000001)
> lr.optimizer.setNumIterations(100)
> or
> LinearRegressionWithSGD.train(input,100,0.00000001)
> To create a reasonable MSE. It took me a while using the dev forum to learn
> that the step size should be really small. Might help save someone the same
> effort when learning mllib.
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