Dev Lakhani created SPARK-5273:
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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.
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