I guess knowing the max alpha is useful to know where to start your grid
search from. However, I think deriving max alpha for NMF should be more
difficult since the problem is non-convex.
Mathieu
On Wed, Feb 3, 2016 at 7:40 AM, Vlad Niculae wrote:
> Hi James,
>
> I'm not sure how useful a minim
Hi James,
I'm not sure how useful a minimum alpha would be. Even if no weights
are shrunk quite to zero, the regularization can still impact
performance metrics. I would be curious what application you have in
mind for this.
The max alpha question is interesting, I am curious as well. (Sorry my
For ElasticNetCV, inside the function _alpha_grid() it computes the maximum
regularization strength alpha, with a given dataset X, target Y, and L1
ratio, for which there will be at least one nonzero coefficient. I'm
wondering if/how the same could be computed for sklearn's L1/L2-regularized
NMF. I
Hi all,
This is not exactly a scikit-learn question but I am trying to send the
output of a model into an exchange of Rabbit MQ.
I will convert the dataframe into json before sending it to the exchange.
This sample code is not sending messages. Any ideas?
#!/usr/bin/env python
import pika
cred
