Comments (2)
I didn't understand what's the problem with the predictions - that's a correct result given the amount of train/test samples. I'm using 5 training samples and 2 test which are being predicted as 3.71 and 3.47.
What's the result that you were expecting?
PS: Kindly reminder that these are the results that libfm
outputs. This repo is just a wrapper.
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Closing since it just seems a misinterpretation of the expected results. Reopen if you have any problems.
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Related Issues (20)
- Path not set HOT 6
- C assertions causing core dump without any useful message HOT 1
- Support dense matrices or convert to sparse instead of raising TypeError HOT 1
- Testing target values HOT 1
- Even if I have set the seed, the result will change? HOT 2
- problem of running in jupyter notebook HOT 8
- Predict for new data. HOT 5
- an Error in Pandas HOT 7
- Model cannot be saved when k2 = 0 HOT 5
- Global bias is None, even k0 is True HOT 2
- how do I run on windows? HOT 9
- Running error with __init__.py HOT 8
- is it possible to use it for Windows computer
- TypeError: init() got an unexpected keyword argument 'seed' HOT 4
- will it work for third order categorical features interaction
- Problem with -save_model on Windows Running toy example HOT 4
- Cannot produce Test(ll) results locally HOT 19
- FM.run in Example code fails on Windows HOT 23
- Predict new data without training. HOT 14
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