Dev Lakhani created SPARK-5273: ---------------------------------- Summary: 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. -- 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