[ https://issues.apache.org/jira/browse/SPARK-5273?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-5273. ------------------------------ Resolution: Fixed Assignee: Sean Owen Fix Version/s: 2.0.0 1.6.1 Resolved by https://github.com/apache/spark/pull/10675 > 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 > Assignee: Sean Owen > Priority: Minor > Fix For: 1.6.1, 2.0.0 > > > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org