Nice to hear that your experiment is consistent to my assumption. The
current L1/L2 will penalize the intercept as well which is not idea.
I'm working on GLMNET in Spark using OWLQN, and I can exactly get the
same solution as R but with scalability in # of rows and columns. Stay
tuned!
Sincerely,
The test accuracy doesn't mean the total loss. All points between (-1,
1) can separate points -1 and +1 and give you 1.0 accuracy, but their
coressponding loss are different. -Xiangrui
On Sun, Sep 28, 2014 at 2:48 AM, Yanbo Liang yanboha...@gmail.com wrote:
Hi
We have used LogisticRegression
Can you check the loss of both LBFGS and SGD implementation? One
reason maybe SGD doesn't converge well and you can see that by
comparing both log-likelihoods. One other potential reason maybe the
label of your training data is totally separable, so you can always
increase the log-likelihood by
Thank you for all your patient response.
I can conclude that if the data is totally separable or over-fit occurs,
weights may be different.
And it also consistent with my experiment.
I have evaluate two different dataset and the result as followed:
Loss function: LogisticGradient
Regularizer: L2
Hi
We have used LogisticRegression with two different optimization method SGD
and LBFGS in MLlib.
With the same dataset and the same training and test split, but get
different weights vector.
For example, we use
spark-1.1.0/data/mllib/sample_binary_classification_data.txt
as our training and
