Comments (4)
I've seen this error before if there are duplicate instances in the dataset. This can cause the matrix in the optimization program to be rank-deficient.
from misvm.
Thanks for the reply!
Is it possible to turn off the optimization? Thx.
Also, I checked my dataset, it seems like there are no duplicated instances.
from misvm.
No, the optimization cannot be turned off; it is fundamental to SVM classification: https://en.wikipedia.org/wiki/Support_vector_machine#Kernel_trick
It looks like the SVM code is being called by mis_stress.py
(not in this repository). It is possible that however the data is manipulated by that code prior to calling the SIL fit
method is leading to this issue.
from misvm.
I have encountered the same issue in my implementation of SVM. I solve the problem by standardizing the data.
from misvm.
Related Issues (17)
- Example Code not Working HOT 1
- the example does not run on my machine HOT 5
- "get_params" of miSVM HOT 1
- Multi label implement HOT 2
- MissSVM result HOT 3
- same sign for all instance-level prediction HOT 5
- Different results in python 2.7.8 and python 3.6.8 HOT 1
- Overflow- INT_MAX reached HOT 2
- Class weights HOT 1
- Prediction scores instead of labels HOT 3
- NaN predicted values HOT 2
- Getting a warning for numerical instability and algorithm is unable to optimize
- Help -- ValueError : Rank(A) < p or Rank([P; A; G]) < n
- Attribute Error running example code HOT 2
- memory error on medium scale HOT 1
- Instance level predictions HOT 5
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