DB Tsai created SPARK-4907:
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Summary: Inconsistent loss and gradient in LeastSquaresGradient
compared with R
Key: SPARK-4907
URL: https://issues.apache.org/jira/browse/SPARK-4907
Project: Spark
Issue Type: Bug
Components: MLlib
Reporter: DB Tsai
In most of the academic paper and algorithm implementations, people use L =
1/2n ||A weights-y||^2 instead of L = 1/n ||A weights-y||^2 for least-squared
loss. See Eq. (1) in http://web.stanford.edu/~hastie/Papers/glmnet.pdf
Since MLlib uses different convention, this will result different residuals and
all the stats properties will be different from GLMNET package in R. The model
coefficients will be still the same under this change.
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