Providing the full StackTrace here:[ code in previous email]
# Tuning hyper-parameters for precision
()
---ValueError
Traceback (most recent call
last) in () 18
scoring='%s_weighte
Hi,
I had almost the same problem you have. I had a subset of the features
believed to be the true features but with unknown coefficients. The other
features may or may not be involved. There is a way to do it by reducing
the penalty term of the selected features, or even make it close to zero.
T
On Sun, Jan 3, 2016 at 6:25 AM, Guoqiang Lan, Mr
wrote:
> But it seem to be not possible to define such a constrained coefficient
> matrix in "sklearn". Am I right?
indeed. You'll need to recode. sklearn lasso only works with in memory
ndarray or sparse matrices.
A
