Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/13796
I found a problem in the merged code: when reg == 0 the minimizer of
softmax cost is not unique.
In such case, it will cause Hessian matrix non-invertible, and I thinks it
may cause quasi-newton's methods such as LBFGS run into numerical problems.
so, is it better to forbid the `reg==0` case for softmax parameters ?
cc @sethah @dbtsai
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