During comparison estimates/ coefficients in 'R'& Spark Mllib of Logistic regression, It has been observed that estimates are not same.
On further investigation, I found that R & Mllib has different implementations for Logistic regression.
R's glm is return a maximum likelihood estimate of the model while Spark's LogisticRegressionWithLBFGS is return a regularized model estimate.
Maximum likelihood estimates seems more efficient, as per literature available.
I am curious to know, why Spark Mllib developers has not chosen 'Maximum likelihood estimation' technique?