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License: BSD 3-Clause "New" or "Revised" License
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pandasasa sendlerlee kiminh kelvict birdgun gucasbrg dukeyuan zhxwmessi plliao yymwater1234 yangyiming1234 huangpingchun zhuyin521 starsnet83 raojun06 casywang chenglongchen shenweichen hengqujushi cwellszhang blancyin batizty crazylook zhouyonglong hangelwen sandy4321 zhaoyulai laisun alwayswaime yuhuichina wanesta yangyw08 soar200 youngyu921 yueyedeai machari youdaodclibffm-ftrl's Issues
how to finetune the parameters when overfitting happens
I'm using the data from real users to make click-through rate prediction.
training sample: 747265
testing sample: 261168
I got training AUC: 0.66824
testing AUC: 0.54578
It seems that the model is overfitting.
how should i finetune the hyper-parameters to avoid overfitting?
which tools of ffm is better?
I found that there are several projects about ffm in your github, such as libffm-ftrl,Difacto_DMLC.Which one would you recommend to use? Do you have any data about their effectiveness and efficiency on benchmark dataset?
features IDs
Is there a need the feature IDs to be unique or is it enough the combination of field + feature ID to be unique?
For example
If we assume that field 1 is movie and field 2 is user and user A has ID 123 and movie B has ID 123
is this a valid row:
1:123:1 2:123:1
as for 2 version ffm(ftrl), which is better or maybe one is not right implemented
https://github.com/superclocks/ffm/blob/master/libffm-ftrl-1.13/ffm.cpp
ffm_long align0 = (ffm_long)model.k*3;
vs https://github.com/CNevd/libffm-ftrl/blob/master/ffm.cpp
_ffm_long align0 = (ffm_long)model.k*2;_
or maybe one is not right implemented?
thanks very much
AUC as optimization function
How hard is to add AUC as a optimization function?
Now the only option is logloss.
libffm or libfm?
I am slightly confused. Is this a factorisation machine or a field-aware factorisation machine?
It seems libffm-ftrl do not support regression, how to change the code to make it support regression?
R.T.
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