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Kernel Ridge result is inconsistent to Gaussian Process Regression
See original GitHub issueDescribe the bug
KernelRidge and GaussianProcessRegressor don’t give the same result even with the same hyper-parametesrs. In theory they should.
Steps/Code to Reproduce
Here is the gist.
https://gist.github.com/lucidfrontier45/2aeb965dd03dc5b82837eceaf194460c
I compared KernelRidge and GaussianProcessRegressor as well as my own Kernel Ridge implementation that use sklearn.gaussian_process.kernels.RBF and GPy’s implementation.
Expected Results
The results of the four models should be identical.
Actual Results
Only the result of sklearn’s KernelRidge is different from the others.
Versions
Linux-5.3.0-40-generic-x86_64-with-debian-buster-sid Python 3.7.3 (default, Mar 27 2019, 22:11:17) [GCC 7.3.0] NumPy 1.17.4 SciPy 1.3.2 Scikit-Learn 0.21.2
Issue Analytics
- State:
- Created 4 years ago
- Comments:9 (3 by maintainers)
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I have no idea 😃 that’s above my pay grade.
@lucidfrontier45
@jgibson2
Thank you so much! Just one more question. Why currently there are two RBF implementations inside sklearn?