Github user MechCoder commented on the pull request:
https://github.com/apache/spark/pull/4906#issuecomment-82982129
@jkbradley I have a silly query that I cannot refrain from asking.
If a point is predicted correctly, then the contribution of log loss due to
that point should be zero right?
However, if we take the log loss function, log(1+exp(â2y * F(x))) , let
us say that y is 1, and F(x) which is the predicted label is also 1, then it
contributes log(1 + exp(-2)) ~ 0.126 to the loss.
The log loss that I am familiar with, i.e log(1 + exp(-y * w' * x)) (where
w is the weight and x is the feature vector), the contribution to the log loss
approaches zero as w * x approaches infinity.
However here w * x is continuous as compared to F(x) which is discrete.
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